diff --git a/.buildinfo b/.buildinfo index d880c06..b0c6685 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 -# This file records the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 59b6df7790291ee8a650d3c7c1c25661 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: eccf12cbfb532c8f88c604652ac49677 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_modules/index.html b/_modules/index.html new file mode 100644 index 0000000..c7aeec7 --- /dev/null +++ b/_modules/index.html @@ -0,0 +1,360 @@ + + + + + + Overview: module code — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Overview: module code
    • +
  • +
    • +
+
+
+
+
+ +

All modules for which code is available

+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/byte_operators.html b/_modules/pycellga/byte_operators.html new file mode 100644 index 0000000..ba4bf1d --- /dev/null +++ b/_modules/pycellga/byte_operators.html @@ -0,0 +1,396 @@ + + + + + + pycellga.byte_operators — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for pycellga.byte_operators

+import ctypes
+
+

+[docs]
+def float_to_bits(float_number: float) -> list[int]:
+    """
+    Convert a float to its bit representation.
+
+    Parameters
+    ----------
+    float_number : float
+        The float number to be converted.
+
+    Returns
+    -------
+    list of int
+        A list of 32 integers (0 or 1) representing the bit pattern of the float.
+    """
+    c_float = ctypes.c_float(float_number)
+    cu = ctypes.c_uint32.from_address(ctypes.addressof(c_float))
+    bit_list = [(cu.value >> i) & 1 for i in range(32)]
+    return bit_list

+
+
+

+[docs]
+def bits_to_float(bit_list: list[int]) -> float:
+    """
+    Convert a bit representation to its float value.
+
+    Parameters
+    ----------
+    bit_list : list of int
+        A list of 32 integers (0 or 1) representing the bit pattern of the float.
+
+    Returns
+    -------
+    float
+        The float value represented by the bit pattern.
+    """
+    u_value = 0
+    for i in range(32):
+        u_value = u_value + bit_list[i] * (2**i)
+    cu = ctypes.c_uint(u_value)
+    c_float = ctypes.c_float.from_address(ctypes.addressof(cu))
+    return c_float.value

+
+
+

+[docs]
+def floats_to_bits(float_list: list[float]) -> list[int]:
+    """
+    Convert a list of floats to their combined bit representation.
+
+    Parameters
+    ----------
+    float_list : list of float
+        The list of float numbers to be converted.
+
+    Returns
+    -------
+    list of int
+        A list of integers (0 or 1) representing the combined bit patterns of the floats.
+    """
+    bit_list = []
+
+    for f in float_list:
+        bit_list = bit_list + float_to_bits(f)
+
+    return bit_list

+
+
+

+[docs]
+def bits_to_floats(bit_list: list[int]) -> list[float]:
+    """
+    Convert a combined bit representation back to a list of floats.
+
+    Parameters
+    ----------
+    bit_list : list of int
+        A list of integers (0 or 1) representing the combined bit patterns of the floats.
+
+    Returns
+    -------
+    list of float
+        The list of float values represented by the bit pattern.
+    """
+    bit_size = len(bit_list)
+    float_size = int(bit_size / 32)
+    float_vector = [0.0] * float_size
+    index = 0
+    findex = 0
+
+    while index + 32 <= bit_size:
+        part = bit_list[index:(index+32)]
+        float_vector[findex] = round(bits_to_float(part), 3)
+        index = index+32
+        findex += 1
+
+    return float_vector

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/grid.html b/_modules/pycellga/grid.html new file mode 100644 index 0000000..55d11cb --- /dev/null +++ b/_modules/pycellga/grid.html @@ -0,0 +1,345 @@ + + + + + + pycellga.grid — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for pycellga.grid

+

+[docs]
+class Grid:
+    """
+    A class to represent a 2D grid.
+
+    Attributes
+    ----------
+    n_rows : int
+        Number of rows in the grid.
+    n_cols : int
+        Number of columns in the grid.
+    """
+
+

+[docs]
+    def __init__(self, n_rows: int, n_cols: int):
+        """
+        Initialize the Grid with the number of rows and columns.
+
+        Parameters
+        ----------
+        n_rows : int
+            Number of rows in the grid.
+        n_cols : int
+            Number of columns in the grid.
+        """
+        self.n_rows = n_rows
+        self.n_cols = n_cols

+
+
+

+[docs]
+    def make_2d_grid(self) -> list:
+        """
+        Create a 2D grid where each cell is represented by a tuple (row, column).
+
+        Returns
+        -------
+        list
+            A list of tuples where each tuple represents a grid cell.
+            Each tuple is of the form (row, column), with rows and columns starting from 1.
+        """
+        grid = []
+        for i in range(self.n_rows):
+            for j in range(self.n_cols):
+                point = (i + 1, j + 1)
+                grid.append(point)
+        return grid

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/individual.html b/_modules/pycellga/individual.html new file mode 100644 index 0000000..90088e9 --- /dev/null +++ b/_modules/pycellga/individual.html @@ -0,0 +1,487 @@ + + + + + + pycellga.individual — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for pycellga.individual

+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+
+from numpy import random
+import numpy as np
+import random as rd
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+
+

+[docs]
+class Individual:
+    """
+    A class to represent an individual in an evolutionary algorithm.
+
+    Attributes
+    ----------
+    chromosome : list
+        The chromosome representing the individual.
+    fitness_value : float
+        The fitness value of the individual.
+    position : tuple
+        The position of the individual, represented as a tuple (x, y).
+    neighbors_positions : list or None
+        The positions of the individual's neighbors.
+    neighbors : list or None
+        The list of neighbors for the individual.
+    gen_type : GeneType
+        The enum type of genome representation (GeneType.BINARY, GeneType.PERMUTATION, GeneType.REAL).
+    ch_size : int
+        The size of the chromosome.
+    """
+
+

+[docs]
+    def __init__(self, 
+                 gen_type: GeneType = GeneType.BINARY, 
+                 ch_size: int = 0,
+                 mins : list[float] = [],
+                 maxs : list[float] = []):
+        """
+        Initialize an Individual with a specific genome type and chromosome size.
+
+        Parameters
+        ----------
+        gen_type : str, optional
+            The type of genome representation. Must be one of GeneType.BINARY, GeneType.PERMUTATION, or GeneType.REAL. (default is GeneType.BINARY)
+        ch_size : int
+            The size of the chromosome.
+        mins: list[float]
+            The minimum values for each gene in the chromosome.
+        maxs: list[float]
+            The maximum values for each gene in the chromosome.
+
+        Description:
+        ------------
+        The Individual class represents an individual in an evolutionary algorithm.
+        If the genome type is BINARY, the chromosome is a list of 0s and 1s.
+        If the genome type is PERMUTATION, the chromosome is a list of integers representing a permutation.
+        In both the binary and permutation cases, ch_size is enought to represent the chromosome.
+        If the genome type is REAL, the chromosome is a list of real numbers.
+        In this case, the mins and maxs lists are used to define the range of each gene in the chromosome.
+        """
+        self.gen_type = gen_type
+        self.ch_size = ch_size
+        self.chromosome = []
+        self.fitness_value = 0
+        self.position = (0, 0)
+        self.neighbors_positions = None
+        self.neighbors = None
+        self.mins = mins
+        self.maxs = maxs

+
+
+
+

+[docs]
+    def randomize(self):
+        """
+        Randomly initialize the chromosome based on the genome type.
+
+        Returns
+        -------
+        list
+            The randomly generated chromosome.
+        
+        Raises
+        ------
+        NotImplementedError
+            If the genome type is not implemented.
+        """
+
+        if self.gen_type == GeneType.BINARY:
+            self.chromosome = [random.randint(2) for i in range(self.ch_size)]
+            
+        elif self.gen_type == GeneType.PERMUTATION:
+            # Generate a random permutation of the numbers 1 to ch_size.
+            # by default random.permutation emits numbers from 0 to ch_size-1
+            # so we add 1 to each number to get the desired range.
+            self.chromosome = list(np.random.permutation(self.ch_size) + 1)
+
+        elif self.gen_type == GeneType.REAL:
+            if len(self.mins) > 0:
+                assert len(self.mins) == len(self.maxs) == self.ch_size
+                self.chromosome = [rd.uniform(self.mins[i], self.maxs[i]) for i in range(self.ch_size)]
+            else:
+                self.chromosome = [rd.uniform(-1.0, 0.1) for i in range(self.ch_size)]
+
+        else:
+            raise NotImplementedError("This gen_type not implemented yet.")
+        return self.chromosome

+
+
+
+

+[docs]
+    def generate_candidate(self, probvector: list) -> list:
+        """
+        Generate a candidate chromosome based on the given probability vector.
+
+        Parameters
+        ----------
+        vector : list of float
+            The probability vector used to generate the candidate chromosome.
+
+        Returns
+        -------
+        list
+            The generated candidate chromosome as a list of 0s and 1s.
+        """
+        ind = [1 if random.rand() < p else 0 for p in probvector]
+        return ind

+
+
+

+[docs]
+    def getneighbors_positions(self) -> list:
+        """
+        Get the positions of the individual's neighbors.
+
+        Returns
+        -------
+        list or None
+            The positions of the individual's neighbors.
+        """
+        return self.neighbors_positions

+
+
+

+[docs]
+    def setneighbors_positions(self, positions: list) -> None:
+        """
+        Set the positions of the individual's neighbors.
+
+        Parameters
+        ----------
+        positions : list
+            The positions to set for the individual's neighbors.
+        """
+        self.neighbors_positions = positions

+
+
+

+[docs]
+    def getneighbors(self) -> list:
+        """
+        Get the list of neighbors for the individual.
+
+        Returns
+        -------
+        list or None
+            The list of neighbors for the individual.
+        """
+        return self.neighbors

+
+
+

+[docs]
+    def setneighbors(self, neighbors: list) -> None:
+        """
+        Set the list of neighbors for the individual.
+
+        Parameters
+        ----------
+        neighbors : list
+            The list of neighbors to set for the individual.
+        """
+        self.neighbors = list(neighbors)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/mutation/bit_flip_mutation.html b/_modules/pycellga/mutation/bit_flip_mutation.html new file mode 100644 index 0000000..d4b42f7 --- /dev/null +++ b/_modules/pycellga/mutation/bit_flip_mutation.html @@ -0,0 +1,367 @@ + + + + + + pycellga.mutation.bit_flip_mutation — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.mutation.bit_flip_mutation
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.mutation.bit_flip_mutation

+import numpy as np
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from mutation.mutation_operator import MutationOperator
+
+

+[docs]
+class BitFlipMutation(MutationOperator):
+    """
+    BitFlipMutation performs a bit flip mutation on an individual in a Genetic Algorithm.
+
+    Parameters
+    ----------
+    mutation_cand : Individual, optional
+        The candidate individual to be mutated (default is None).
+    problem : AbstractProblem, optional
+        The problem instance that provides the fitness function (default is None).
+    """
+
+

+[docs]
+    def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
+        """
+        Initialize the BitFlipMutation object.
+
+        Parameters
+        ----------
+        mutation_cand : Individual, optional
+            The candidate individual to be mutated (default is None).
+        problem : AbstractProblem, optional
+            The problem instance that provides the fitness function (default is None).
+        """
+        self.mutation_cand = mutation_cand
+        self.problem = problem

+
+
+

+[docs]
+    def mutate(self) -> Individual:
+        """
+        Perform a bit flip mutation on the candidate individual.
+
+        A single bit in the candidate's chromosome is randomly selected and flipped
+        (i.e., a 0 is changed to a 1, or a 1 is changed to a 0).
+
+        Returns
+        -------
+        Individual
+            A new individual with the mutated chromosome.
+        """
+        # Convert the chromosome to a list to allow mutation
+        m_ch = list(self.mutation_cand.chromosome)
+        
+        # Randomly select an index in the chromosome
+        index = np.random.randint(0, len(m_ch))
+        
+        # Flip the bit at the selected index
+        if m_ch[index] == 0:
+            m_ch[index] = 1
+        else:
+            m_ch[index] = 0
+
+        # Create a new Individual with the mutated chromosome
+        mutated = Individual()
+        mutated.chromosome = m_ch
+        mutated.ch_size = len(m_ch)
+        mutated.position = self.mutation_cand.position
+        mutated.neighbors_positions = self.mutation_cand.neighbors_positions
+        mutated.fitness_value = self.problem.f(mutated.chromosome)
+
+        return mutated

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/mutation/byte_mutation.html b/_modules/pycellga/mutation/byte_mutation.html new file mode 100644 index 0000000..8762bea --- /dev/null +++ b/_modules/pycellga/mutation/byte_mutation.html @@ -0,0 +1,393 @@ + + + + + + pycellga.mutation.byte_mutation — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.mutation.byte_mutation
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.mutation.byte_mutation

+import numpy as np
+from individual import *
+from problems.abstract_problem import AbstractProblem
+import struct
+from mutation.mutation_operator import MutationOperator
+
+

+[docs]
+class ByteMutation(MutationOperator):
+    """
+    ByteMutation operator defined in (Satman, 2013). ByteMutation performs a byte-wise mutation 
+    on an individual's chromosome in a Genetic Algorithm.
+
+    Parameters
+    ----------
+    mutation_cand : Individual, optional
+        The candidate individual to be mutated (default is None).
+    problem : AbstractProblem, optional
+        The problem instance that provides the fitness function (default is None).
+    """
+
+

+[docs]
+    def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
+        """
+        Initialize the ByteMutation object.
+
+        Parameters
+        ----------
+        mutation_cand : Individual, optional
+            The candidate individual to be mutated (default is None).
+        problem : AbstractProblem, optional
+            The problem instance that provides the fitness function (default is None).
+        """
+        self.mutation_cand = mutation_cand
+        self.problem = problem

+
+
+

+[docs]
+    def mutate(self) -> Individual:
+        """
+        Perform a byte-wise mutation on the candidate individual.
+
+        A single byte in one of the candidate's chromosome's floating-point numbers is randomly selected
+        and either incremented or decremented by 1, wrapping around if necessary.
+
+        Returns
+        -------
+        Individual
+            A new individual with the mutated chromosome.
+        """
+        # Convert the chromosome to a list to allow mutation
+        m_ch = list(self.mutation_cand.chromosome)
+
+        # Randomly select an index in the chromosome
+        index = np.random.randint(0, len(m_ch))
+
+        # Pack the selected float into bytes
+        m_byte_ch = list(struct.pack("d", m_ch[index]))
+
+        # Randomly select an index in the byte representation
+        rnd_index = np.random.randint(0, len(m_byte_ch))
+
+        # Get the byte at the selected index
+        mutant = m_byte_ch[rnd_index]
+
+        # Generate a random uniform value to decide increment or decrement
+        u = np.random.uniform(0, 1)
+
+        # Increment or decrement the byte, with wrapping
+        if u < 0.5:
+            mutant += 1
+        else:
+            mutant -= 1
+        if mutant > 255:
+            mutant = 0
+        elif mutant < 0:
+            mutant = 255
+
+        # Replace the selected byte with the mutated byte
+        m_byte_ch[rnd_index] = mutant
+
+        # Convert the mutated byte sequence back to a float
+        mutatnt_part_byte = bytearray(m_byte_ch)
+        mutant_part_float = list(struct.unpack("d", mutatnt_part_byte))
+        m_ch[index] = round(mutant_part_float[0], 5)
+
+        # Create a new Individual with the mutated chromosome
+        mutated = Individual()
+        mutated.chromosome = m_ch
+        mutated.ch_size = len(m_ch)
+        mutated.position = self.mutation_cand.position
+        mutated.neighbors_positions = self.mutation_cand.neighbors_positions
+        mutated.fitness_value = self.problem.f(mutated.chromosome)
+
+        return mutated

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/mutation/byte_mutation_random.html b/_modules/pycellga/mutation/byte_mutation_random.html new file mode 100644 index 0000000..cc3fe6d --- /dev/null +++ b/_modules/pycellga/mutation/byte_mutation_random.html @@ -0,0 +1,380 @@ + + + + + + pycellga.mutation.byte_mutation_random — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.mutation.byte_mutation_random
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.mutation.byte_mutation_random

+import numpy as np
+from individual import *
+from problems.abstract_problem import AbstractProblem
+import struct
+from mutation.mutation_operator import MutationOperator
+
+

+[docs]
+class ByteMutationRandom(MutationOperator):
+    """
+    ByteMutationRandom operator defined in (Satman, 2013). ByteMutationRandom performs 
+    a random byte mutation on an individual's chromosome in a Genetic Algorithm.
+
+    Parameters
+    ----------
+    mutation_cand : Individual, optional
+        The candidate individual to be mutated (default is None).
+    problem : AbstractProblem, optional
+        The problem instance that provides the fitness function (default is None).
+    """
+
+

+[docs]
+    def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
+        """
+        Initialize the ByteMutationRandom object.
+
+        Parameters
+        ----------
+        mutation_cand : Individual, optional
+            The candidate individual to be mutated (default is None).
+        problem : AbstractProblem, optional
+            The problem instance that provides the fitness function (default is None).
+        """
+        self.mutation_cand = mutation_cand
+        self.problem = problem

+
+
+

+[docs]
+    def mutate(self) -> Individual:
+        """
+        Perform a random byte mutation on the candidate individual.
+
+        A single byte in one of the candidate's chromosome's floating-point numbers is randomly selected
+        and mutated to a random value.
+
+        Returns
+        -------
+        Individual
+            A new individual with the mutated chromosome.
+        """
+        # Convert the chromosome to a list to allow mutation
+        m_ch = list(self.mutation_cand.chromosome)
+
+        # Randomly select an index in the chromosome
+        index = np.random.randint(0, len(m_ch))
+
+        # Pack the selected float into bytes
+        m_byte_ch = list(struct.pack("d", m_ch[index]))
+
+        # Randomly select an index in the byte representation
+        rnd_index = np.random.randint(0, len(m_byte_ch))
+
+        # Generate a random byte
+        mutant = np.random.randint(0, 256)
+
+        # Replace the selected byte with the random byte
+        m_byte_ch[rnd_index] = mutant
+
+        # Convert the mutated byte sequence back to a float
+        mutatnt_part_byte = bytearray(m_byte_ch)
+        mutant_part_float = list(struct.unpack("d", mutatnt_part_byte))
+        m_ch[index] = round(mutant_part_float[0], 5)
+
+        # Create a new Individual with the mutated chromosome
+        mutated = Individual()
+        mutated.chromosome = m_ch
+        mutated.ch_size = len(m_ch)
+        mutated.position = self.mutation_cand.position
+        mutated.neighbors_positions = self.mutation_cand.neighbors_positions
+        mutated.fitness_value = self.problem.f(mutated.chromosome)
+
+        return mutated

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/mutation/float_uniform_mutation.html b/_modules/pycellga/mutation/float_uniform_mutation.html new file mode 100644 index 0000000..755af58 --- /dev/null +++ b/_modules/pycellga/mutation/float_uniform_mutation.html @@ -0,0 +1,367 @@ + + + + + + pycellga.mutation.float_uniform_mutation — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.mutation.float_uniform_mutation
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.mutation.float_uniform_mutation

+import random
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from mutation.mutation_operator import MutationOperator
+
+

+[docs]
+class FloatUniformMutation(MutationOperator):
+    """
+    FloatUniformMutation performs a uniform mutation on an individual's chromosome in a Genetic Algorithm.
+
+    Parameters
+    ----------
+    mutation_cand : Individual, optional
+        The candidate individual to be mutated (default is None).
+    problem : AbstractProblem, optional
+        The problem instance that provides the fitness function (default is None).
+    """
+
+

+[docs]
+    def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
+        """
+        Initialize the FloatUniformMutation object.
+
+        Parameters
+        ----------
+        mutation_cand : Individual, optional
+            The candidate individual to be mutated (default is None).
+        problem : AbstractProblem, optional
+            The problem instance that provides the fitness function (default is None).
+        """
+        self.mutation_cand = mutation_cand
+        self.problem = problem

+
+
+

+[docs]
+    def mutate(self) -> Individual:
+        """
+        Perform a uniform mutation on the candidate individual.
+
+        Each gene in the candidate's chromosome is mutated by adding or subtracting a random float uniformly
+        sampled from [0, 1]. The mutation is rounded to 5 decimal places.
+
+        Returns
+        -------
+        Individual
+            A new individual with the mutated chromosome.
+        """
+        # Convert the chromosome to a list to allow mutation
+        m_ch = list(self.mutation_cand.chromosome)
+
+        # Mutate each gene in the chromosome
+        for i in range(len(m_ch)):
+            rnd = random.uniform(0, 1)
+            if rnd < 0.5:
+                m_ch[i] = round(m_ch[i] - rnd, 5)
+            else:
+                m_ch[i] = round(m_ch[i] + rnd, 5)
+
+        # Create a new Individual with the mutated chromosome
+        mutated_ch_new = list(m_ch)
+        mutated = Individual()
+        mutated.chromosome = mutated_ch_new
+        mutated.ch_size = len(mutated_ch_new)
+        mutated.position = self.mutation_cand.position
+        mutated.neighbors_positions = self.mutation_cand.neighbors_positions
+        mutated.fitness_value = self.problem.f(mutated.chromosome)
+
+        return mutated

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/mutation/insertion_mutation.html b/_modules/pycellga/mutation/insertion_mutation.html new file mode 100644 index 0000000..ad77507 --- /dev/null +++ b/_modules/pycellga/mutation/insertion_mutation.html @@ -0,0 +1,369 @@ + + + + + + pycellga.mutation.insertion_mutation — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.mutation.insertion_mutation
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.mutation.insertion_mutation

+import numpy as np
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from mutation.mutation_operator import MutationOperator
+
+

+[docs]
+class InsertionMutation(MutationOperator):
+    """
+    InsertionMutation performs an insertion mutation on an individual's chromosome in a Genetic Algorithm.
+
+    Parameters
+    ----------
+    mutation_cand : Individual, optional
+        The candidate individual to be mutated (default is None).
+    problem : AbstractProblem, optional
+        The problem instance that provides the fitness function (default is None).
+    """
+
+

+[docs]
+    def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
+        """
+        Initialize the InsertionMutation object.
+
+        Parameters
+        ----------
+        mutation_cand : Individual, optional
+            The candidate individual to be mutated (default is None).
+        problem : AbstractProblem, optional
+            The problem instance that provides the fitness function (default is None).
+        """
+        self.mutation_cand = mutation_cand
+        self.problem = problem

+
+
+

+[docs]
+    def mutate(self) -> Individual:
+        """
+        Perform an insertion mutation on the candidate individual.
+
+        A gene in the candidate's chromosome is randomly selected and moved to a new position in the chromosome.
+
+        Returns
+        -------
+        Individual
+            A new individual with the mutated chromosome.
+        """
+        # Convert the chromosome to a list to allow mutation
+        mutated_ch = list(self.mutation_cand.chromosome)
+
+        # Randomly select two distinct indices in the chromosome
+        ran_1 = np.random.randint(0, len(mutated_ch))
+        ran_2 = np.random.randint(0, len(mutated_ch))
+        while ran_1 == ran_2:
+            ran_2 = np.random.randint(0, len(mutated_ch))
+
+        # Remove the gene at the first index and insert it at the second index
+        x = mutated_ch[ran_1]
+        mutated_ch_new = list(mutated_ch)
+        mutated_ch_new.remove(x)
+        mutated_ch_new.insert(ran_2, x)
+
+        # Create a new Individual with the mutated chromosome
+        mutated = Individual()
+        mutated.chromosome = mutated_ch_new
+        mutated.ch_size = len(mutated_ch_new)
+        mutated.position = self.mutation_cand.position
+        mutated.neighbors_positions = self.mutation_cand.neighbors_positions
+        mutated.fitness_value = self.problem.f(mutated.chromosome)
+
+        return mutated

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/mutation/shuffle_mutation.html b/_modules/pycellga/mutation/shuffle_mutation.html new file mode 100644 index 0000000..de99b2d --- /dev/null +++ b/_modules/pycellga/mutation/shuffle_mutation.html @@ -0,0 +1,382 @@ + + + + + + pycellga.mutation.shuffle_mutation — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.mutation.shuffle_mutation
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.mutation.shuffle_mutation

+import numpy as np
+import random as rd
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from mutation.mutation_operator import MutationOperator
+
+

+[docs]
+class ShuffleMutation(MutationOperator):
+    """
+    ShuffleMutation performs a shuffle mutation on an individual's chromosome in a Genetic Algorithm.
+
+    Parameters
+    ----------
+    mutation_cand : Individual, optional
+        The candidate individual to be mutated (default is None).
+    problem : AbstractProblem, optional
+        The problem instance that provides the fitness function (default is None).
+    """
+
+

+[docs]
+    def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
+        """
+        Initialize the ShuffleMutation object.
+
+        Parameters
+        ----------
+        mutation_cand : Individual, optional
+            The candidate individual to be mutated (default is None).
+        problem : AbstractProblem, optional
+            The problem instance that provides the fitness function (default is None).
+        """
+        self.mutation_cand = mutation_cand
+        self.problem = problem

+
+
+

+[docs]
+    def mutate(self) -> Individual:
+        """
+        Perform a shuffle mutation on the candidate individual.
+
+        A subsequence of genes in the candidate's chromosome is randomly selected and shuffled.
+
+        Returns
+        -------
+        Individual
+            A new individual with the mutated chromosome.
+        """
+        # Convert the chromosome to a list to allow mutation
+        mutated_ch = list(self.mutation_cand.chromosome)
+
+        # Randomly select two distinct indices in the chromosome with a minimum distance between them
+        ran_1 = np.random.randint(0, len(mutated_ch))
+        ran_2 = np.random.randint(0, len(mutated_ch))
+
+        while abs(ran_1 - ran_2) < 3:
+            ran_2 = np.random.randint(0, len(mutated_ch))
+
+        # Create a new list to hold the mutated chromosome
+        mutated_ch_new = list(mutated_ch)
+
+        # Shuffle the subsequence between the two selected indices
+        if ran_1 < ran_2:
+            part = mutated_ch_new[ran_1:ran_2]
+            part_shuffle = rd.sample(part, len(part))
+            while part == part_shuffle:
+                part_shuffle = rd.sample(part, len(part))
+            mutated_ch_new[ran_1:ran_2] = part_shuffle
+        else:
+            part = mutated_ch_new[ran_2:ran_1]
+            part_shuffle = rd.sample(part, len(part))
+            while part == part_shuffle:
+                part_shuffle = rd.sample(part, len(part))
+            mutated_ch_new[ran_2:ran_1] = part_shuffle
+
+        # Create a new Individual with the mutated chromosome
+        mutated = Individual()
+        mutated.chromosome = mutated_ch_new
+        mutated.ch_size = len(mutated_ch_new)
+        mutated.position = self.mutation_cand.position
+        mutated.neighbors_positions = self.mutation_cand.neighbors_positions
+        mutated.fitness_value = self.problem.f(mutated.chromosome)
+
+        return mutated

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/mutation/swap_mutation.html b/_modules/pycellga/mutation/swap_mutation.html new file mode 100644 index 0000000..de7ba2a --- /dev/null +++ b/_modules/pycellga/mutation/swap_mutation.html @@ -0,0 +1,370 @@ + + + + + + pycellga.mutation.swap_mutation — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.mutation.swap_mutation
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.mutation.swap_mutation

+import numpy as np
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from mutation.mutation_operator import MutationOperator
+
+

+[docs]
+class SwapMutation(MutationOperator):
+    """
+    SwapMutation performs a swap mutation on an individual's chromosome in a Genetic Algorithm.
+
+    Parameters
+    ----------
+    mutation_cand : Individual, optional
+        The candidate individual to be mutated (default is None).
+    problem : AbstractProblem, optional
+        The problem instance that provides the fitness function (default is None).
+    """
+
+

+[docs]
+    def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
+        """
+        Initialize the SwapMutation object.
+
+        Parameters
+        ----------
+        mutation_cand : Individual, optional
+            The candidate individual to be mutated (default is None).
+        problem : AbstractProblem, optional
+            The problem instance that provides the fitness function (default is None).
+        """
+        self.mutation_cand = mutation_cand
+        self.problem = problem

+
+
+

+[docs]
+    def mutate(self) -> Individual:
+        """
+        Perform a swap mutation on the candidate individual.
+
+        Two genes in the candidate's chromosome are randomly selected and swapped.
+
+        Returns
+        -------
+        Individual
+            A new individual with the mutated chromosome.
+        """
+        # Convert the chromosome to a list to allow mutation
+        mutated_ch = list(self.mutation_cand.chromosome)
+
+        # Randomly select two distinct indices in the chromosome
+        ran_1 = np.random.randint(0, len(mutated_ch))
+        ran_2 = np.random.randint(0, len(mutated_ch))
+        while ran_1 == ran_2:
+            ran_2 = np.random.randint(0, len(mutated_ch))
+
+        # Swap the genes at the selected indices
+        swap_candidate_1 = mutated_ch[ran_1]
+        swap_candidate_2 = mutated_ch[ran_2]
+        mutated_ch_new = list(mutated_ch)
+        mutated_ch_new[ran_1] = swap_candidate_2
+        mutated_ch_new[ran_2] = swap_candidate_1
+
+        # Create a new Individual with the mutated chromosome
+        mutated = Individual()
+        mutated.chromosome = mutated_ch_new
+        mutated.ch_size = len(mutated_ch_new)
+        mutated.position = self.mutation_cand.position
+        mutated.neighbors_positions = self.mutation_cand.neighbors_positions
+        mutated.fitness_value = self.problem.f(mutated.chromosome)
+
+        return mutated

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/mutation/two_opt_mutation.html b/_modules/pycellga/mutation/two_opt_mutation.html new file mode 100644 index 0000000..2d13e7a --- /dev/null +++ b/_modules/pycellga/mutation/two_opt_mutation.html @@ -0,0 +1,373 @@ + + + + + + pycellga.mutation.two_opt_mutation — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.mutation.two_opt_mutation
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.mutation.two_opt_mutation

+import numpy as np
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from mutation.mutation_operator import MutationOperator
+
+

+[docs]
+class TwoOptMutation(MutationOperator):
+    """
+    TwoOptMutation performs a 2-opt mutation on an individual's chromosome in a Genetic Algorithm.
+
+    Parameters
+    ----------
+    mutation_cand : Individual, optional
+        The candidate individual to be mutated (default is None).
+    problem : AbstractProblem, optional
+        The problem instance that provides the fitness function (default is None).
+    """
+
+

+[docs]
+    def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
+        """
+        Initialize the TwoOptMutation object.
+
+        Parameters
+        ----------
+        mutation_cand : Individual, optional
+            The candidate individual to be mutated (default is None).
+        problem : AbstractProblem, optional
+            The problem instance that provides the fitness function (default is None).
+        """
+        self.mutation_cand = mutation_cand
+        self.problem = problem

+
+
+

+[docs]
+    def mutate(self) -> Individual:
+        """
+        Perform a 2-opt mutation on the candidate individual.
+
+        A segment of the candidate's chromosome is randomly selected and reversed.
+
+        Returns
+        -------
+        Individual
+            A new individual with the mutated chromosome.
+        """
+        # Convert the chromosome to a list to allow mutation
+        mutated_ch = list(self.mutation_cand.chromosome)
+
+        # Randomly select two distinct indices in the chromosome with a minimum distance between them
+        ran_1 = np.random.randint(0, len(mutated_ch))
+        ran_2 = np.random.randint(0, len(mutated_ch))
+
+        while abs(ran_1 - ran_2) < 3:
+            ran_2 = np.random.randint(0, len(mutated_ch))
+
+        # Create a new list to hold the mutated chromosome
+        mutated_ch_new = list(mutated_ch)
+
+        # Reverse the segment between the two selected indices
+        if ran_1 < ran_2:
+            mutated_ch_new[ran_1:ran_2] = reversed(mutated_ch_new[ran_1:ran_2])
+        else:
+            mutated_ch_new[ran_2:ran_1] = reversed(mutated_ch_new[ran_2:ran_1])
+
+        # Create a new Individual with the mutated chromosome
+        mutated = Individual()
+        mutated.chromosome = mutated_ch_new
+        mutated.ch_size = len(mutated_ch_new)
+        mutated.position = self.mutation_cand.position
+        mutated.neighbors_positions = self.mutation_cand.neighbors_positions
+        mutated.fitness_value = self.problem.f(mutated.chromosome)
+
+        return mutated

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/neighborhoods/compact_13.html b/_modules/pycellga/neighborhoods/compact_13.html new file mode 100644 index 0000000..c07ed4b --- /dev/null +++ b/_modules/pycellga/neighborhoods/compact_13.html @@ -0,0 +1,380 @@ + + + + + + pycellga.neighborhoods.compact_13 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.neighborhoods.compact_13
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.neighborhoods.compact_13

+

+[docs]
+class Compact13:
+    """
+    Compact13 calculates the positions of the 12 neighbors in a 2D grid for a given position,
+    considering wrapping at the grid edges.
+
+    Parameters
+    ----------
+    position : tuple
+        The (x, y) position of the point whose neighbors are to be calculated.
+    n_rows : int
+        The number of rows in the grid.
+    n_cols : int
+        The number of columns in the grid.
+    """
+
+

+[docs]
+    def __init__(self, position, n_rows, n_cols):
+        """
+        Initialize the Compact13 object.
+
+        Parameters
+        ----------
+        position : tuple
+            The (x, y) position of the point whose neighbors are to be calculated.
+        n_rows : int
+            The number of rows in the grid.
+        n_cols : int
+            The number of columns in the grid.
+        """
+        self.position = position
+        self.n_rows = n_rows
+        self.n_cols = n_cols

+
+
+

+[docs]
+    def calculate_neighbors_positions(self) -> list:
+        """
+        Calculate the positions of the 12 neighbors for the given position in the grid.
+
+        The neighbors are determined by considering wrapping at the grid edges.
+
+        Returns
+        -------
+        list
+            A list of tuples representing the positions of the 12 neighbors.
+        """
+        neighbors_positions = []
+        point = self.position
+        x = point[0]
+        y = point[1]
+
+        # Change in x and y for the 12 neighbors
+        dx = [-2, -1, -1, -1, 0, 0, 0, 0, 1, 1, 1, 2]
+        dy = [0, -1, 0, 1, -2, -1, 1, 2, -1, 0, 1, 0]
+
+        if x == self.n_rows or y == self.n_rows:
+            for i in range(len(dx)):
+                npx = (x + dx[i]) % self.n_rows
+                npy = (y + dy[i]) % self.n_rows
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        elif x != self.n_rows or y != self.n_rows:
+            for i in range(len(dx)):
+                npx = x + dx[i]
+                npy = y + dy[i]
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        return neighbors_positions

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/neighborhoods/compact_21.html b/_modules/pycellga/neighborhoods/compact_21.html new file mode 100644 index 0000000..819b408 --- /dev/null +++ b/_modules/pycellga/neighborhoods/compact_21.html @@ -0,0 +1,380 @@ + + + + + + pycellga.neighborhoods.compact_21 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.neighborhoods.compact_21
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.neighborhoods.compact_21

+

+[docs]
+class Compact21:
+    """
+    Compact21 calculates the positions of the 20 neighbors in a 2D grid for a given position,
+    considering wrapping at the grid edges.
+
+    Parameters
+    ----------
+    position : tuple
+        The (x, y) position of the point whose neighbors are to be calculated.
+    n_rows : int
+        The number of rows in the grid.
+    n_cols : int
+        The number of columns in the grid.
+    """
+
+

+[docs]
+    def __init__(self, position, n_rows, n_cols):
+        """
+        Initialize the Compact21 object.
+
+        Parameters
+        ----------
+        position : tuple
+            The (x, y) position of the point whose neighbors are to be calculated.
+        n_rows : int
+            The number of rows in the grid.
+        n_cols : int
+            The number of columns in the grid.
+        """
+        self.position = position
+        self.n_rows = n_rows
+        self.n_cols = n_cols

+
+
+

+[docs]
+    def calculate_neighbors_positions(self) -> list:
+        """
+        Calculate the positions of the 20 neighbors for the given position in the grid.
+
+        The neighbors are determined by considering wrapping at the grid edges.
+
+        Returns
+        -------
+        list
+            A list of tuples representing the positions of the 20 neighbors.
+        """
+        neighbors_positions = []
+        point = self.position
+        x = point[0]
+        y = point[1]
+
+        # Change in x and y for the 20 neighbors
+        dx = [-2, -2, -2, -1, -1, -1, -1, -1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
+        dy = [-1, 0, 1, -2, -1, 0, 1, 2, -2, -1, 1, 2, -2, -1, 0, 1, 2, -1, 0, 1]
+
+        if x == self.n_rows or y == self.n_rows:
+            for i in range(len(dx)):
+                npx = (x + dx[i]) % self.n_rows
+                npy = (y + dy[i]) % self.n_rows
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        elif x != self.n_rows or y != self.n_rows:
+            for i in range(len(dx)):
+                npx = x + dx[i]
+                npy = y + dy[i]
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        return neighbors_positions

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/neighborhoods/compact_25.html b/_modules/pycellga/neighborhoods/compact_25.html new file mode 100644 index 0000000..86b93a5 --- /dev/null +++ b/_modules/pycellga/neighborhoods/compact_25.html @@ -0,0 +1,380 @@ + + + + + + pycellga.neighborhoods.compact_25 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.neighborhoods.compact_25
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.neighborhoods.compact_25

+

+[docs]
+class Compact25:
+    """
+    Compact25 calculates the positions of the 24 neighbors in a 2D grid for a given position,
+    considering wrapping at the grid edges.
+
+    Parameters
+    ----------
+    position : tuple
+        The (x, y) position of the point whose neighbors are to be calculated.
+    n_rows : int
+        The number of rows in the grid.
+    n_cols : int
+        The number of columns in the grid.
+    """
+
+

+[docs]
+    def __init__(self, position, n_rows, n_cols):
+        """
+        Initialize the Compact25 object.
+
+        Parameters
+        ----------
+        position : tuple
+            The (x, y) position of the point whose neighbors are to be calculated.
+        n_rows : int
+            The number of rows in the grid.
+        n_cols : int
+            The number of columns in the grid.
+        """
+        self.position = position
+        self.n_rows = n_rows
+        self.n_cols = n_cols

+
+
+

+[docs]
+    def calculate_neighbors_positions(self) -> list:
+        """
+        Calculate the positions of the 24 neighbors for the given position in the grid.
+
+        The neighbors are determined by considering wrapping at the grid edges.
+
+        Returns
+        -------
+        list
+            A list of tuples representing the positions of the 24 neighbors.
+        """
+        neighbors_positions = []
+        point = self.position
+        x = point[0]
+        y = point[1]
+
+        # Change in x and y for the 24 neighbors
+        dx = [-2, -2, -2, -2, -2, -1, -1, -1, -1, -1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
+        dy = [-2, -1, 0, 1, 2, -2, -1, 0, 1, 2, -2, -1, 1, 2, -2, -1, 0, 1, 2, -2, -1, 0, 1, 2]
+
+        if x == self.n_rows or y == self.n_rows:
+            for i in range(len(dx)):
+                npx = (x + dx[i]) % self.n_rows
+                npy = (y + dy[i]) % self.n_rows
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        elif x != self.n_rows or y != self.n_rows:
+            for i in range(len(dx)):
+                npx = x + dx[i]
+                npy = y + dy[i]
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        return neighbors_positions

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/neighborhoods/compact_9.html b/_modules/pycellga/neighborhoods/compact_9.html new file mode 100644 index 0000000..cd975d6 --- /dev/null +++ b/_modules/pycellga/neighborhoods/compact_9.html @@ -0,0 +1,380 @@ + + + + + + pycellga.neighborhoods.compact_9 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.neighborhoods.compact_9
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.neighborhoods.compact_9

+

+[docs]
+class Compact9:
+    """
+    Compact9 calculates the positions of the 8 neighbors in a 2D grid for a given position,
+    considering wrapping at the grid edges.
+
+    Parameters
+    ----------
+    position : tuple
+        The (x, y) position of the point whose neighbors are to be calculated.
+    n_rows : int
+        The number of rows in the grid.
+    n_cols : int
+        The number of columns in the grid.
+    """
+
+

+[docs]
+    def __init__(self, position, n_rows, n_cols):
+        """
+        Initialize the Compact9 object.
+
+        Parameters
+        ----------
+        position : tuple
+            The (x, y) position of the point whose neighbors are to be calculated.
+        n_rows : int
+            The number of rows in the grid.
+        n_cols : int
+            The number of columns in the grid.
+        """
+        self.position = position
+        self.n_rows = n_rows
+        self.n_cols = n_cols

+
+
+

+[docs]
+    def calculate_neighbors_positions(self) -> list:
+        """
+        Calculate the positions of the 8 neighbors for the given position in the grid.
+
+        The neighbors are determined by considering wrapping at the grid edges.
+
+        Returns
+        -------
+        list
+            A list of tuples representing the positions of the 8 neighbors.
+        """
+        neighbors_positions = []
+        point = self.position
+        x = point[0]
+        y = point[1]
+
+        # Change in x and y for the 8 neighbors
+        dx = [-1, -1, -1, 0, 1, 1, 1, 0]
+        dy = [-1, 0, 1, -1, -1, 0, 1, 1]
+
+        if x == self.n_rows or y == self.n_rows:
+            for i in range(len(dx)):
+                npx = (x + dx[i]) % self.n_rows
+                npy = (y + dy[i]) % self.n_rows
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        elif x != self.n_rows or y != self.n_rows:
+            for i in range(len(dx)):
+                npx = x + dx[i]
+                npy = y + dy[i]
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        return neighbors_positions

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/neighborhoods/linear_5.html b/_modules/pycellga/neighborhoods/linear_5.html new file mode 100644 index 0000000..0c33c5f --- /dev/null +++ b/_modules/pycellga/neighborhoods/linear_5.html @@ -0,0 +1,380 @@ + + + + + + pycellga.neighborhoods.linear_5 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.neighborhoods.linear_5
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.neighborhoods.linear_5

+

+[docs]
+class Linear5:
+    """
+    Linear5 calculates the positions of the 4 neighbors in a 2D grid for a given position,
+    considering wrapping at the grid edges.
+
+    Parameters
+    ----------
+    position : tuple
+        The (x, y) position of the point whose neighbors are to be calculated.
+    n_rows : int
+        The number of rows in the grid.
+    n_cols : int
+        The number of columns in the grid.
+    """
+
+

+[docs]
+    def __init__(self, position, n_rows, n_cols):
+        """
+        Initialize the Linear5 object.
+
+        Parameters
+        ----------
+        position : tuple
+            The (x, y) position of the point whose neighbors are to be calculated.
+        n_rows : int
+            The number of rows in the grid.
+        n_cols : int
+            The number of columns in the grid.
+        """
+        self.position = position
+        self.n_rows = n_rows
+        self.n_cols = n_cols

+
+
+

+[docs]
+    def calculate_neighbors_positions(self) -> list:
+        """
+        Calculate the positions of the 4 neighbors for the given position in the grid.
+
+        The neighbors are determined by considering wrapping at the grid edges.
+
+        Returns
+        -------
+        list
+            A list of tuples representing the positions of the 4 neighbors.
+        """
+        neighbors_positions = []
+        point = self.position
+        x = point[0]
+        y = point[1]
+
+        # Change in x and y for the 4 neighbors
+        dx = [0, 0, -1, 1]
+        dy = [1, -1, 0, 0]
+
+        if x == self.n_rows or y == self.n_rows:
+            for i in range(len(dx)):
+                npx = (x + dx[i]) % self.n_rows
+                npy = (y + dy[i]) % self.n_rows
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        elif x != self.n_rows or y != self.n_rows:
+            for i in range(len(dx)):
+                npx = x + dx[i]
+                npy = y + dy[i]
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        return neighbors_positions

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/neighborhoods/linear_9.html b/_modules/pycellga/neighborhoods/linear_9.html new file mode 100644 index 0000000..e404bb5 --- /dev/null +++ b/_modules/pycellga/neighborhoods/linear_9.html @@ -0,0 +1,382 @@ + + + + + + pycellga.neighborhoods.linear_9 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.neighborhoods.linear_9
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.neighborhoods.linear_9

+

+[docs]
+class Linear9:
+    """
+    Linear9 calculates the positions of the 8 neighbors in a 2D grid for a given position,
+    considering wrapping at the grid edges.
+
+    Parameters
+    ----------
+    position : tuple
+        The (x, y) position of the point whose neighbors are to be calculated.
+    n_rows : int
+        The number of rows in the grid.
+    n_cols : int
+        The number of columns in the grid.
+    """
+
+

+[docs]
+    def __init__(self, position, n_rows, n_cols):
+        """
+        Initialize the Linear9 object.
+
+        Parameters
+        ----------
+        position : tuple
+            The (x, y) position of the point whose neighbors are to be calculated.
+        n_rows : int
+            The number of rows in the grid.
+        n_cols : int
+            The number of columns in the grid.
+        """
+        self.position = position
+        self.n_rows = n_rows
+        self.n_cols = n_cols

+
+
+

+[docs]
+    def calculate_neighbors_positions(self) -> list:
+        """
+        Calculate the positions of the 8 neighbors for the given position in the grid.
+
+        The neighbors are determined by considering wrapping at the grid edges.
+
+        Returns
+        -------
+        list
+            A list of tuples representing the positions of the 8 neighbors.
+        """
+        neighbors_positions = []
+        point = self.position
+        x = point[0]
+        y = point[1]
+
+        # Change in x and y for the 8 neighbors
+        dx = [-2, -1, 0, 0, 1, 2, 0, 0]
+        dy = [0, 0, -2, -1, 0, 0, 1, 2]
+
+        # If the position is at the edge of the grid, wrap around
+        if x == self.n_rows or y == self.n_rows:
+            for i in range(len(dx)):
+                npx = (x + dx[i]) % self.n_rows
+                npy = (y + dy[i]) % self.n_rows
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        # If the position is not at the edge, calculate normally
+        elif x != self.n_rows or y != self.n_rows:
+            for i in range(len(dx)):
+                npx = x + dx[i]
+                npy = y + dy[i]
+                if npx == 0:
+                    npx = self.n_rows
+                if npy == 0:
+                    npy = self.n_rows
+
+                neighbor_position = (npx, npy)
+                neighbors_positions.append(neighbor_position)
+
+        return neighbors_positions

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/optimizer.html b/_modules/pycellga/optimizer.html new file mode 100644 index 0000000..1abf817 --- /dev/null +++ b/_modules/pycellga/optimizer.html @@ -0,0 +1,1167 @@ + + + + + + pycellga.optimizer — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for pycellga.optimizer

+
+import random
+import numpy as np
+import byte_operators
+from population import *
+from individual import *
+from mutation.bit_flip_mutation import *
+from selection.tournament_selection import *
+from recombination.one_point_crossover import *
+from problems.single_objective.discrete.binary.one_max import *
+
+from typing import Callable, List, Tuple
+from collections.abc import Callable
+
+
+from dataclasses import dataclass
+from typing import List, Callable
+
+

+[docs]
+@dataclass
+class Result:
+    chromosome: List[float]
+    fitness_value: float
+    generation_found: int

+
+
+

+[docs]
+def cga(
+    n_cols: int,
+    n_rows: int,
+    n_gen: int,
+    ch_size: int,
+    p_crossover: float,
+    p_mutation: float,
+    problem: AbstractProblem,
+    selection: SelectionOperator,
+    recombination: RecombinationOperator,
+    mutation: MutationOperator,
+    seed_par: int = None
+) -> Result:
+    """
+    Optimize the given problem using a genetic algorithm.
+
+    Parameters
+    ----------
+    n_cols : int
+        Number of columns in the population grid.
+    n_rows : int
+        Number of rows in the population grid.
+    n_gen : int
+        Number of generations to evolve.
+    ch_size : int
+        Size of the chromosome.
+    gen_type : str
+        Type of the genome representation (e.g., 'Binary', 'Permutation', 'Real').
+    p_crossover : float
+        Probability of crossover (between 0 and 1).
+    p_mutation : float
+        Probability of mutation (between 0 and 1).
+    problem : AbstractProblem
+        The problem instance used for fitness evaluation.
+    selection : SelectionOperator
+        Function or class used for selecting parents.
+    recombination : RecombinationOperator
+        Function or class used for recombination (crossover).
+    mutation : MutationOperator
+        Function or class used for mutation.
+    mins : list[float]
+        List of minimum values for each gene in the chromosome (for real value optimization).
+    maxs : list[float]
+        List of maximum values for each gene in the chromosome (for real value optimization).
+    seed_par : int
+        Ensures the random number generation is repeatable.
+
+    Returns
+    -------
+    Result
+        A Result object containing the best solution found, with its chromosome, fitness value, and generation.
+    """
+
+    if seed_par is not None:
+        np.random.seed(seed_par)
+        random.seed(seed_par)
+
+    pop_size = n_cols * n_rows
+    best_solutions = []
+    best_objectives = []
+    avg_objectives = []
+    method_name = OptimizationMethod.CGA
+
+    # Generate Initial Population
+    pop_list = Population(
+                method_name, 
+                ch_size, 
+                n_rows, 
+                n_cols,
+                gen_type = problem.gen_type, 
+                problem = problem,
+                mins = problem.xl,
+                maxs = problem.xu).initial_population()
+
+    pop_list_ordered = sorted(pop_list, key=lambda x: x.fitness_value)
+
+    best_solutions.append(pop_list_ordered[0].chromosome)
+    best_objectives.append(pop_list_ordered[0].fitness_value)
+    best_ever_solution = Result(
+        chromosome=pop_list_ordered[0].chromosome,
+        fitness_value=pop_list_ordered[0].fitness_value,
+        generation_found=0
+    )
+
+    mean = sum(ind.fitness_value for ind in pop_list) / len(pop_list)
+    avg_objectives.append(mean)
+
+    # Evolutionary Algorithm Loop
+    generation = 1
+    while generation != n_gen + 1:
+        for c in range(pop_size):
+            offsprings = []
+            parents = selection(pop_list, c).get_parents()
+            rnd = np.random.rand()
+
+            if rnd < p_crossover:
+                offsprings = recombination(parents, problem).get_recombinations()
+            else:
+                offsprings = parents
+
+            for p in range(len(offsprings)):
+                mutation_cand = offsprings[p]
+                rnd = np.random.rand()
+
+                if rnd < p_mutation:
+                    mutated = mutation(mutation_cand, problem).mutate()
+                    offsprings[p] = mutated
+
+                # Replacement: Replace if better
+                if offsprings[p].fitness_value < parents[p].fitness_value:
+                    index = pop_list.index(parents[p])
+                    pop_list[index] = offsprings[p]
+
+        pop_list_ordered = sorted(pop_list, key=lambda x: x.fitness_value)
+        best_solutions.append(pop_list_ordered[0].chromosome)
+        best_objectives.append(pop_list_ordered[0].fitness_value)
+
+        if pop_list_ordered[0].fitness_value < best_ever_solution.fitness_value:
+            best_ever_solution = Result(
+                chromosome=pop_list_ordered[0].chromosome,
+                fitness_value=pop_list_ordered[0].fitness_value,
+                generation_found=generation
+            )
+
+        mean = sum(ind.fitness_value for ind in pop_list) / len(pop_list)
+        avg_objectives.append(mean)
+        generation += 1
+    
+    return best_ever_solution

+
+
+

+[docs]
+def sync_cga(
+    n_cols: int,
+    n_rows: int,
+    n_gen: int,
+    ch_size: int,
+    p_crossover: float,
+    p_mutation: float,
+    problem: Callable[[List[float]], float],
+    selection: SelectionOperator,
+    recombination: RecombinationOperator,
+    mutation: MutationOperator,
+    seed_par: int = None
+) -> Result:
+    """
+    Optimize the given problem using a synchronous cellular genetic algorithm (Sync-CGA).
+
+    Parameters
+    ----------
+    n_cols : int
+        Number of columns in the population grid.
+    n_rows : int
+        Number of rows in the population grid.
+    n_gen : int
+        Number of generations to evolve.
+    ch_size : int
+        Size of the chromosome.
+    gen_type : str
+        Type of the genome representation (e.g., 'Binary', 'Permutation', 'Real').
+    p_crossover : float
+        Probability of crossover between parents.
+    p_mutation : float
+        Probability of mutation in offspring.
+    problem : Callable[[List[float]], float]
+        Function to evaluate the fitness of a solution. Takes a list of floats and returns a float.
+    selection : SelectionOperator
+        Function or class used for selecting parents.
+    recombination : RecombinationOperator
+        Function or class used for recombination (crossover).
+    mutation : MutationOperator
+        Function or class used for mutation.
+    mins : List[float]
+        List of minimum values for each gene in the chromosome (for real value optimization).
+    maxs : List[float]
+        List of maximum values for each gene in the chromosome (for real value optimization).
+    seed_par : int
+        Ensures the random number generation is repeatable.
+        
+    Returns
+    -------
+    Result
+        A Result object containing the best solution found, with its chromosome, fitness value, and generation.
+    """
+
+    if seed_par is not None:
+        np.random.seed(seed_par)
+        random.seed(seed_par)
+
+    pop_size = n_cols * n_rows
+    best_solutions = []
+    best_objectives = []
+    avg_objectives = []
+    method_name = OptimizationMethod.SYNCGA
+
+    # Generate Initial Population
+    pop_list = Population(
+                method_name, 
+                ch_size, 
+                n_rows, 
+                n_cols,
+                gen_type = problem.gen_type, 
+                problem = problem,
+                mins = problem.xl,
+                maxs = problem.xu).initial_population()
+
+    # Sort population by fitness value
+    pop_list_ordered = sorted(pop_list, key=lambda x: x.fitness_value)
+
+    # Track the best solution in the initial population
+    best_solutions.append(pop_list_ordered[0].chromosome)
+    best_objectives.append(pop_list_ordered[0].fitness_value)
+    best_ever_solution = Result(
+        chromosome=pop_list_ordered[0].chromosome,
+        fitness_value=pop_list_ordered[0].fitness_value,
+        generation_found=0
+    )
+
+    # Calculate the mean fitness for the initial population
+    mean = sum(map(lambda x: x.fitness_value, pop_list)) / len(pop_list)
+    avg_objectives.append(mean)
+
+    # Evolutionary Algorithm Loop
+    generation = 1
+    aux_poplist = []
+
+    while generation != n_gen + 1:
+        aux_poplist = pop_list.copy()  # Create a copy of the population for synchronous update
+
+        for c in range(pop_size):
+            offsprings = []
+            # Select parents
+            parents = selection(pop_list, c).get_parents()
+            rnd = np.random.rand()
+
+            # Apply crossover with probability p_crossover
+            if rnd < p_crossover:
+                offsprings = recombination(parents, problem).get_recombinations()
+            else:
+                offsprings = parents
+
+            for p in range(len(offsprings)):
+                mutation_cand = offsprings[p]
+                rnd = np.random.rand()
+
+                # Apply mutation with probability p_mutation
+                if rnd < p_mutation:
+                    mutated = mutation(mutation_cand, problem).mutate()
+                    offsprings[p] = mutated
+
+                # Replacement: Replace if offspring is better
+                if offsprings[p].fitness_value < parents[p].fitness_value:
+                    index = pop_list.index(parents[p])
+                    aux_poplist[index] = offsprings[p]
+
+        # Update population synchronously
+        pop_list = aux_poplist
+        pop_list_ordered = sorted(pop_list, key=lambda x: x.fitness_value)
+
+        # Track best solutions
+        best_solutions.append(pop_list_ordered[0].chromosome)
+        best_objectives.append(pop_list_ordered[0].fitness_value)
+
+        # Update best ever solution if the current solution is better
+        if pop_list_ordered[0].fitness_value < best_ever_solution.fitness_value:
+            best_ever_solution = Result(
+                chromosome=pop_list_ordered[0].chromosome,
+                fitness_value=pop_list_ordered[0].fitness_value,
+                generation_found=generation
+            )
+
+        # Calculate the mean fitness for the current generation
+        mean = sum(map(lambda x: x.fitness_value, pop_list)) / len(pop_list)
+        avg_objectives.append(mean)
+
+        generation += 1
+
+    return best_ever_solution

+
+
+

+[docs]
+def alpha_cga(
+    n_cols: int,
+    n_rows: int,
+    n_gen: int,
+    ch_size: int,
+    p_crossover: float,
+    p_mutation: float,
+    problem: AbstractProblem,
+    selection: SelectionOperator,
+    recombination: RecombinationOperator,
+    mutation: MutationOperator,
+    seed_par: int = None
+) -> Result:
+    """
+    Optimize a problem using an evolutionary algorithm with an alpha-male exchange mechanism.
+
+    Parameters
+    ----------
+    n_cols : int
+        Number of columns in the grid for the population.
+    n_rows : int
+        Number of rows in the grid for the population.
+    n_gen : int
+        Number of generations to run the optimization.
+    ch_size : int
+        Size of the chromosome.
+    gen_type : GeneType
+        Type of genome representation (GeneType.BINARY, GeneType.PERMUTATION, or GeneType.REAL).
+    p_crossover : float
+        Probability of crossover, should be between 0 and 1.
+    p_mutation : float
+        Probability of mutation, should be between 0 and 1.
+    problem : AbstractProblem
+        The problem instance used to evaluate fitness.
+    selection : SelectionOperator
+        Function used for selection in the evolutionary algorithm.
+    recombination : RecombinationOperator
+        Function used for recombination (crossover) in the evolutionary algorithm.
+    mutation : MutationOperator
+        Function used for mutation in the evolutionary algorithm.
+    mins : List[float]
+        List of minimum values for each gene in the chromosome (for real value optimization).
+    maxs : List[float]
+        List of maximum values for each gene in the chromosome (for real value optimization).
+    seed_par : int
+        Ensures the random number generation is repeatable.
+
+    Returns
+    -------
+    Result
+        A Result object containing the best solution found, with its chromosome, fitness value, and generation.
+    """
+
+    if seed_par is not None:
+        np.random.seed(seed_par)
+        random.seed(seed_par)
+
+    pop_size = n_cols * n_rows
+    best_solutions = []
+    best_objectives = []
+    avg_objectives = []
+    method_name = OptimizationMethod.ALPHA_CGA
+
+    # Generate Initial Population
+    pop_list = Population(
+                method_name, 
+                ch_size, 
+                n_rows, 
+                n_cols,
+                gen_type = problem.gen_type, 
+                problem = problem,
+                mins = problem.xl,
+                maxs = problem.xu).initial_population()
+    
+    # Sort population by fitness value
+    pop_list_ordered = sorted(pop_list, key=lambda x: x.fitness_value)
+
+    # Initialize tracking of best solutions
+    best_solutions.append(pop_list_ordered[0].chromosome)
+    best_objectives.append(pop_list_ordered[0].fitness_value)
+    best_ever_solution = Result(
+        chromosome=pop_list_ordered[0].chromosome,
+        fitness_value=pop_list_ordered[0].fitness_value,
+        generation_found=0
+    )
+
+    # Calculate mean fitness for the initial population
+    mean = sum(map(lambda x: x.fitness_value, pop_list)) / len(pop_list)
+    avg_objectives.append(mean)
+
+    # Optimization loop
+    generation = 1
+
+    while generation != n_gen + 1:
+        for c in range(0, pop_size, n_cols):
+            # Alpha-male exchange mechanism
+            if generation % 10 == 0:
+                rnd1 = rd.randrange(1, pop_size + 1, n_cols)
+                rnd2 = rd.randrange(1, pop_size + 1, n_cols)
+                while rnd1 == rnd2:
+                    rnd2 = np.random.randint(1, n_cols + 1)
+
+                alpha_male1 = pop_list[rnd1]
+                alpha_male2 = pop_list[rnd2]
+
+                pop_list[rnd2] = alpha_male1
+                pop_list[rnd1] = alpha_male2
+
+            for n in range(n_cols):
+                offsprings = []
+                parents = []
+
+                p1 = pop_list[c]
+                p2 = pop_list[c + n]
+                parents.append(p1)
+                parents.append(p2)
+
+                rnd = np.random.rand()
+
+                # Apply crossover with probability p_crossover
+                if rnd < p_crossover:
+                    offsprings = recombination(parents, problem).get_recombinations()
+                else:
+                    offsprings = parents
+
+                for p in range(len(offsprings)):
+
+                    mutation_cand = offsprings[p]
+                    rnd = np.random.rand()
+
+                    # Apply mutation with probability p_mutation
+                    if rnd < p_mutation:
+                        mutated = mutation(mutation_cand, problem).mutate()
+                        offsprings[p] = mutated
+
+                    # Replacement: Replace if offspring is better
+                    if offsprings[p].fitness_value < parents[1].fitness_value:
+                        try:
+                            index = pop_list.index(parents[1])
+                        except ValueError:
+                            continue
+                        new_p = offsprings[p]
+                        pop_list[index] = new_p
+
+        # Sort population and update best solutions
+        pop_list_ordered = sorted(pop_list, key=lambda x: x.fitness_value)
+
+        best_solutions.append(pop_list_ordered[0].chromosome)
+        best_objectives.append(pop_list_ordered[0].fitness_value)
+
+        # Update best ever solution if current solution is better
+        if pop_list_ordered[0].fitness_value < best_ever_solution.fitness_value:
+            best_ever_solution = Result(
+                chromosome=pop_list_ordered[0].chromosome,
+                fitness_value=pop_list_ordered[0].fitness_value,
+                generation_found=generation
+            )
+
+        # Update average objectives
+        mean = sum(map(lambda x: x.fitness_value, pop_list)) / len(pop_list)
+        avg_objectives.append(mean)
+
+        generation += 1
+
+    return best_ever_solution

+
+
+

+[docs]
+def ccga(
+    n_cols: int,
+    n_rows: int,
+    n_gen: int,
+    ch_size: int,
+    problem: AbstractProblem,
+    selection: SelectionOperator
+) -> Result:
+    """
+    Perform optimization using a Cooperative Coevolutionary Genetic Algorithm (CCGA).
+
+    Parameters
+    ----------
+    n_cols : int
+        Number of columns in the grid for the population.
+    n_rows : int
+        Number of rows in the grid for the population.
+    n_gen : int
+        Number of generations to run the optimization.
+    ch_size : int
+        Size of the chromosome.
+    gen_type : GeneType
+        Type of genome representation (GeneType.BINARY, Genetype.PERMUTATION, GeneType.REAL).
+    problem : AbstractProblem
+        The problem instance used to evaluate fitness.
+    selection : SelectionOperator
+        Function used for selection in the evolutionary algorithm.
+    mins : List[float]
+        List of minimum values for each gene in the chromosome (for real value optimization).
+    maxs : List[float]
+        List of maximum values for each gene in the chromosome (for real value optimization).
+
+    Returns
+    -------
+    Result
+        A Result object containing the best solution found during the optimization process,
+        including its chromosome, fitness value, and generation.
+    """
+
+    pop_size = n_cols * n_rows
+    best_solutions = []
+    best_objectives = []
+    avg_objectives = []
+    vector = [0.5 for _ in range(ch_size)]
+    method_name = OptimizationMethod.CCGA
+
+    # Generate Initial Population
+    pop_list = Population(
+                method_name, 
+                ch_size, 
+                n_rows, 
+                n_cols,
+                gen_type = problem.gen_type, 
+                problem = problem,
+                mins = problem.xl,
+                maxs = problem.xu).initial_population()
+
+    # Sort population by fitness value
+    pop_list_ordered = sorted(pop_list, key=lambda x: x.fitness_value)
+
+    # Initialize tracking of best solutions
+    best_solutions.append(pop_list_ordered[0].chromosome)
+    best_objectives.append(pop_list_ordered[0].fitness_value)
+    best_ever_solution = Result(
+        chromosome=pop_list_ordered[0].chromosome,
+        fitness_value=pop_list_ordered[0].fitness_value,
+        generation_found=0
+    )
+
+    # Calculate mean fitness for the initial population
+    mean = sum(map(lambda x: x.fitness_value, pop_list)) / len(pop_list)
+    avg_objectives.append(mean)
+    best = pop_list_ordered[0]
+
+    # Evolutionary Algorithm Loop
+    generation = 1
+    while generation != n_gen + 1:
+        for c in range(pop_size):
+            # Select parents and determine the winner and loser
+            parents = selection(pop_list, c).get_parents()
+            p1, p2 = parents[0], parents[1]
+            winner, loser = compete(p1, p2)
+
+            if winner.fitness_value > best.fitness_value:
+                best = winner
+
+            # Update the vector based on the winner and loser
+            update_vector(vector, winner, loser, pop_size)
+
+            # Re-generate the population based on the updated vector
+            pop_list = Population(
+                method_name, 
+                ch_size, 
+                n_rows, 
+                n_cols,
+                gen_type = problem.gen_type, 
+                problem = problem,
+                mins = problem.xl,
+                maxs = problem.xu).initial_population()
+
+        # Update best ever solution if the current best solution is better
+        if best.fitness_value > best_ever_solution.fitness_value:
+            best_ever_solution = Result(
+                chromosome=best.chromosome,
+                fitness_value=best.fitness_value,
+                generation_found=generation
+            )
+
+        # Update average objectives
+        mean = sum(map(lambda x: x.fitness_value, pop_list)) / len(pop_list)
+        avg_objectives.append(mean)
+
+        generation += 1
+
+    return best_ever_solution

+
+
+

+[docs]
+def mcccga(
+    n_cols: int,
+    n_rows: int,
+    n_gen: int,
+    ch_size: int,
+    problem: AbstractProblem,
+    selection: SelectionOperator
+) -> Result:
+    """
+    Optimize the given problem using a multi-population machine-coded compact genetic algorithm (MCCGA).
+
+    Parameters
+    ----------
+    n_cols : int
+        Number of columns in the population grid.
+    n_rows : int
+        Number of rows in the population grid.
+    n_gen : int
+        Number of generations to evolve.
+    ch_size : int
+        Size of the chromosome.
+    problem : AbstractProblem
+        Problem instance for fitness evaluation.
+    selection : SelectionOperator
+        Function or class used for selecting parents.
+
+    Returns
+    -------
+    Result
+        A Result instance containing the best solution found during optimization,
+        including its chromosome, fitness value, and generation found.
+    """
+
+    pop_size = n_cols * n_rows
+    best_objectives = []
+    avg_objectives = []
+    method_name = OptimizationMethod.MCCCGA
+
+    mins = problem.xl
+    maxs = problem.xu
+
+    # Generate initial probability vector
+    vector = generate_probability_vector(mins, maxs, pop_size)
+
+    # Generate Initial Population
+    pop_list = Population(
+                method_name, 
+                ch_size, 
+                n_rows, 
+                n_cols,
+                problem.gen_type, 
+                problem, 
+                vector).initial_population()
+
+
+    # Sort population by fitness value
+    pop_list_ordered = sorted(pop_list, key=lambda x: x.fitness_value)
+
+    # Track the best solution in the initial population
+    best_objectives.append(pop_list_ordered[0].fitness_value)
+    best_byte_ch = byte_operators.bits_to_floats(pop_list_ordered[0].chromosome)
+    best_ever_solution = Result(
+        chromosome=best_byte_ch,
+        fitness_value=pop_list_ordered[0].fitness_value,
+        generation_found=1
+    )
+
+    # Calculate the mean fitness for the initial population
+    mean = sum(map(lambda x: x.fitness_value, pop_list)) / len(pop_list)
+    avg_objectives.append(mean)
+    best = pop_list_ordered[0]
+
+    # Evolutionary Algorithm Loop
+    generation = 1
+    while generation != n_gen + 1:
+        for c in range(pop_size):
+
+            # Select parents from the population
+            parents = selection(pop_list, c).get_parents()
+            p1, p2 = parents[0], parents[1]
+
+            # Compete parents and identify the winner and loser
+            winner, loser = compete(p1, p2)
+            if winner.fitness_value < best.fitness_value:
+                best = winner
+
+            # Update the probability vector based on the competition result
+            update_vector(vector, winner, loser, pop_size)
+
+        # Re-generate the population based on the updated vector
+        pop_list = Population(
+                method_name, 
+                ch_size, 
+                n_rows, 
+                n_cols,
+                problem.gen_type, 
+                problem, 
+                vector).initial_population()
+
+        # Track the best fitness value and update the best solution if necessary
+        best_objectives.append(best.fitness_value)
+        best_byte_ch = byte_operators.bits_to_floats(pop_list_ordered[0].chromosome)
+
+        if best.fitness_value < best_ever_solution.fitness_value:
+            best_ever_solution = Result(
+                chromosome=best_byte_ch,
+                fitness_value=best.fitness_value,
+                generation_found=generation
+            )
+
+        # Calculate the mean fitness for the current generation
+        mean = sum(map(lambda x: x.fitness_value, pop_list)) / len(pop_list)
+        avg_objectives.append(mean)
+        best_byte_ch = byte_operators.bits_to_floats(best.chromosome)
+
+        generation += 1
+
+    # Evaluate the final solution sampled from the probability vector
+    best_byte_ch = byte_operators.bits_to_floats(sample(vector))
+    best_byte_result = problem.f(best_byte_ch)
+
+    # Update the best-ever solution if the sampled solution is better
+    if best_byte_result <= best_ever_solution.fitness_value:
+        best_ever_solution = Result(
+            chromosome=best_byte_ch,
+            fitness_value=best_byte_result,
+            generation_found=generation
+        )
+
+    return best_ever_solution

+
+
+

+[docs]
+def compete(p1: Individual, p2: Individual) -> Tuple[Individual, Individual]:
+    """
+    Compete between two individuals to determine the better one.
+
+    Parameters
+    ----------
+    p1 : Individual
+        First individual.
+    p2 : Individual
+        Second individual.
+
+    Returns
+    -------
+    Tuple[Individual, Individual]
+        The better individual and the loser.
+    """
+    if p1.fitness_value < p2.fitness_value:
+        return p1, p2
+    else:
+        return p2, p1

+
+
+
+

+[docs]
+def update_vector(vector: List[float], winner: Individual, loser: Individual, pop_size: int):
+    """
+    Update the probability vector based on the winner and loser individuals.
+
+    Parameters
+    ----------
+    vector : List[float]
+        Probability vector to be updated.
+    winner : Individual
+        The winning individual.
+    loser : Individual
+        The losing individual.
+    pop_size : int
+        Size of the population.
+    """
+    for i in range(len(vector)):
+        if winner.chromosome[i] != loser.chromosome[i]:
+            if winner.chromosome[i] == 1:
+                vector[i] += round((1.0 / float(pop_size)), 3)
+            else:
+                vector[i] -= round((1.0 / float(pop_size)), 3)

+
+
+
+

+[docs]
+def random_vector_between(mins: List[float], maxs: List[float]) -> List[float]:
+    """
+    Generate a random vector of floats between the given minimum and maximum values.
+
+    Parameters
+    ----------
+    mins : List[float]
+        List of minimum values.
+    maxs : List[float]
+        List of maximum values.
+
+    Returns
+    -------
+    List[float]
+        Randomly generated vector.
+    """
+    n = len(mins)
+    result = [0.0] * n
+
+    for i in range(n):
+        result[i] = mins[i] + random.random() * (maxs[i] - mins[i])
+
+    return result

+
+
+
+

+[docs]
+def generate_probability_vector(mins: List[float], maxs: List[float], ntries: int) -> List[float]:
+    """
+    Generate a probability vector based on the given minimum and maximum values.
+
+    Parameters
+    ----------
+    mins : List[float]
+        List of minimum values.
+    maxs : List[float]
+        List of maximum values.
+    ntries : int
+        Number of trials for generating the probability vector.
+
+    Returns
+    -------
+    List[float]
+        Probability vector.
+    """
+    nbits = len(mins) * 32
+    mutrate = 1.0 / ntries
+    probvector = [0.0] * nbits
+
+    for _ in range(ntries):
+        floats = random_vector_between(mins, maxs)
+        floatbits = byte_operators.floats_to_bits(floats)
+        for k in range(nbits):
+            if floatbits[k] == 1:
+                probvector[k] = probvector[k] + mutrate
+
+    return probvector

+
+
+

+[docs]
+def sample(probvector: List[float]) -> List[int]:
+    """
+    Sample a vector based on the provided probability vector.
+
+    Parameters
+    ----------
+    probvector : List[float]
+        Probability vector for sampling.
+
+    Returns
+    -------
+    List[int]
+        Sampled binary vector.
+    """
+    n = len(probvector)
+    newvector = [0] * n
+
+    for i in range(n):
+        if random.random() < probvector[i]:
+            newvector[i] = 1
+
+    return newvector

+
+
+
+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/population.html b/_modules/pycellga/population.html new file mode 100644 index 0000000..d22c707 --- /dev/null +++ b/_modules/pycellga/population.html @@ -0,0 +1,431 @@ + + + + + + pycellga.population — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for pycellga.population

+from typing import List
+from individual import *
+from grid import *
+from neighborhoods.linear_9 import Linear9
+from byte_operators import *
+from problems.abstract_problem import AbstractProblem
+from enum import Enum 
+
+
+

+[docs]
+class OptimizationMethod(Enum):
+    """
+    OptimizationMethod is an enumeration class that represents the optimization methods used in an evolutionary algorithm.
+    The five optimization methods are CGA, SYNCGA, ALPHA_CGA, CCGA, and MCCCGA.
+    "cga", "sync_cga", "alpha_cga", "ccga", "mcccga"
+    """
+    CGA = 1
+    SYNCGA = 2
+    ALPHA_CGA = 3
+    CCGA = 4
+    MCCCGA = 5

+
+
+
+

+[docs]
+class Population:
+    """
+    A class to represent a population in an evolutionary algorithm.
+
+    Attributes
+    ----------
+    method_name : OptimizationMethod
+        The name of the optimization method. Must be one of OptimizationMethod.CGA, OptimizationMethod.SYNCGA, OptimizationMethod.ALPHA_CGA, OptimizationMethod.CCGA, or OptimizationMethod.MCCCGA.
+    ch_size : int
+        The size of the chromosome.
+    n_rows : int
+        The number of rows in the grid.
+    n_cols : int
+        The number of columns in the grid.
+    gen_type : str
+        The type of genome representation (GeneType.BINARY, Genetype.PERMUTATION, GeneType.REAL).
+    problem : AbstractProblem
+        The problem instance used to evaluate fitness.
+    vector : list
+        A list used to generate candidates for the population (relevant for MCCCGA).
+    """
+

+[docs]
+    def __init__(self, 
+                 method_name: OptimizationMethod = OptimizationMethod.CGA, 
+                 ch_size: int = 0, 
+                 n_rows: int = 0, 
+                 n_cols: int = 0, 
+                 gen_type: str = "", 
+                 problem: AbstractProblem = None, 
+                 vector: list = [],
+                 mins : list[float] = [],
+                 maxs : list[float] = []):
+        """
+        Initialize the Population with the specified parameters.
+
+        Parameters
+        ----------
+        method_name : OptimizationMethod.
+            The name of the optimization method. Must be one of OptimizationMethod.CGA, OptimizationMethod.SYNCGA, OptimizationMethod.ALPHA_CGA, OptimizationMethod.CCGA, or OptimizationMethod.MCCCGA.
+            Default is OptimizationMethod.CGA.
+        ch_size : int, optional
+            The size of the chromosome (default is 0).
+        n_rows : int, optional
+            The number of rows in the grid (default is 0).
+        n_cols : int, optional
+            The number of columns in the grid (default is 0).
+        gen_type : str, optional
+            The type of genome representation (default is an empty string).
+        problem : AbstractProblem, optional
+            The problem instance used to evaluate fitness (default is None).
+        vector : list, optional
+            A list used to generate candidates (default is an empty list).
+        mins: list[float]
+            The minimum values for each gene in the chromosome (for real value optimization).
+        maxs: list[float]
+            The maximum values for each gene in the chromosome (for real value optimization).
+        """
+        self.method_name = method_name
+        self.ch_size = ch_size
+        self.n_rows = n_rows
+        self.n_cols = n_cols
+        self.gen_type = gen_type
+        self.problem = problem
+        self.vector = vector
+        self.mins = mins
+        self.maxs = maxs

+
+
+

+[docs]
+    def initial_population(self) -> List[Individual]:
+        """
+        Generate the initial population of individuals.
+
+        Returns
+        -------
+        List[Individual]
+            A list of initialized `Individual` objects with their respective chromosomes, fitness values, positions, and neighbors.
+        """
+        pop_size = self.n_rows * self.n_cols
+        pop_list = []
+
+        grid = Grid(self.n_rows, self.n_cols).make_2d_grid()
+
+        for i in range(pop_size):
+            ind = Individual(gen_type = self.gen_type, ch_size = self.ch_size, 
+                             mins = self.mins, maxs = self.maxs)
+                
+            # Initialize chromosome and evaluate fitness for cga, syn_cga and alpha_cga
+            if self.method_name in [OptimizationMethod.CGA, OptimizationMethod.SYNCGA, OptimizationMethod.ALPHA_CGA, OptimizationMethod.CCGA]:
+                ind.chromosome = ind.randomize()
+                ind.fitness_value = self.problem.f(ind.chromosome)
+
+            # Initialize chromosome and evaluate fitness for cga and mcccga
+            elif self.method_name in [OptimizationMethod.MCCCGA]:
+                ind.chromosome = ind.generate_candidate(self.vector)
+                ind_byte_ch = bits_to_floats(ind.chromosome)
+                ind.fitness_value = self.problem.f(ind_byte_ch)
+
+            ind.position = grid[i]
+            ind.neighbors_positions = Linear9(
+                position=ind.position, n_rows=self.n_rows, n_cols=self.n_cols
+            ).calculate_neighbors_positions()
+            ind.neighbors = None
+            pop_list.append(ind)
+
+        return pop_list

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/abstract_problem.html b/_modules/pycellga/problems/abstract_problem.html new file mode 100644 index 0000000..06195d5 --- /dev/null +++ b/_modules/pycellga/problems/abstract_problem.html @@ -0,0 +1,394 @@ + + + + + + pycellga.problems.abstract_problem — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.abstract_problem
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.abstract_problem

+from abc import ABC, abstractmethod
+from typing import List, Any
+from pymoo.core.problem import Problem
+from common import GeneType
+
+

+[docs]
+class AbstractProblem(Problem, ABC):
+    """
+    Abstract base class for defining optimization problems compatible with pymoo.
+
+    This class provides a structure for defining optimization problems, where the
+    user specifies the gene type, the number of variables, and their bounds. It includes
+    an abstract method `f` for evaluating the fitness of a solution, which must be
+    implemented by subclasses.
+
+    Attributes
+    ----------
+    gen_type : GeneType
+        The type of genes used in the problem (e.g., REAL, BINARY).
+    n_var : int
+        The number of design variables.
+    xl : List[float] or numpy.ndarray
+        The lower bounds for the design variables.
+    xu : List[float] or numpy.ndarray
+        The upper bounds for the design variables.
+
+    Methods
+    -------
+    f(x: List[Any]) -> float
+        Abstract method to compute the fitness value for a given solution. 
+        Must be implemented by subclasses.
+    evaluate(x, out, *args, **kwargs)
+        Computes the objective value(s) for pymoo's optimization framework.
+    """
+
+

+[docs]
+    def __init__(self, gen_type: GeneType, n_var, xl, xu):
+        """
+        Initialize the AbstractProblem with gene type, variable count, and bounds.
+
+        Parameters
+        ----------
+        gen_type : Any
+            The type of genes used in the problem (e.g., REAL, BINARY).
+        n_var : int
+            The number of design variables.
+        xl : float 
+            The lower bound for the design variables.
+        xu : float
+            The upper bound for the design variables.
+        """
+        self.gen_type = gen_type
+
+        super().__init__(n_var=n_var, n_obj=1, n_constr=0, xl=xl, xu=xu)

+
+
+

+[docs]
+    @abstractmethod
+    def f(self, x: List[Any]) -> float:
+        """
+        Abstract method for evaluating the fitness of a solution.
+
+        This method must be implemented by subclasses to define the objective function
+        of the optimization problem.
+
+        Parameters
+        ----------
+        x : list
+            List of design variable values.
+        
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        raise NotImplementedError("Subclasses should implement this method.")

+
+
+

+[docs]
+    def evaluate(self, x, out, *args, **kwargs):
+        """
+        Evaluate function for compatibility with pymoo's optimizer.
+
+        This method wraps the `f` method and allows pymoo to handle batch evaluations
+        by storing the computed fitness values in the output dictionary.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/ackley.html b/_modules/pycellga/problems/single_objective/continuous/ackley.html new file mode 100644 index 0000000..7dd6e08 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/ackley.html @@ -0,0 +1,381 @@ + + + + + + pycellga.problems.single_objective.continuous.ackley — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.ackley
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.ackley

+from numpy import pi, e, cos, sqrt, exp, power
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Ackley(AbstractProblem):
+    """
+    Ackley function implementation for optimization problems.
+
+    The Ackley function is widely used for testing optimization algorithms.
+    It is characterized by a nearly flat outer region and a large hole at the center.
+    The function is usually evaluated on the hypercube x_i ∈ [-32.768, 32.768], for all i = 1, 2, ..., d.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bound for each variable (fixed to -32.768).
+    xu : float
+        Upper bound for each variable (fixed to 32.768).
+
+    Methods
+    -------
+    __init__(n_var: int)
+        Initialize the Ackley problem with the specified number of variables.
+
+    f(x: List[float]) -> float
+        Compute the Ackley function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+    """
+
+

+[docs]
+    def __init__(self, n_var):
+        """
+        Initialize the Ackley problem.
+
+        Parameters
+        ----------
+        n_var : int
+            Number of variables (dimensions) in the problem.
+        """
+        gen_type = GeneType.REAL
+        xl = -32.768
+        xu = 32.768
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Ackley function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        sum1 = sum(power(gene, 2) for gene in x)
+        sum2 = sum(cos(2 * pi * gene) for gene in x)
+
+        fitness = -20.0 * exp(-0.2 * sqrt(sum1 / self.n_var)) - exp(sum2 / self.n_var) + 20.0 + e
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/bentcigar.html b/_modules/pycellga/problems/single_objective/continuous/bentcigar.html new file mode 100644 index 0000000..ab7927a --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/bentcigar.html @@ -0,0 +1,373 @@ + + + + + + pycellga.problems.single_objective.continuous.bentcigar — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.bentcigar
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.bentcigar

+from problems.abstract_problem import AbstractProblem
+from mpmath import power as pw
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Bentcigar(AbstractProblem):
+    """
+    Bentcigar function implementation for optimization problems.
+
+    The Bentcigar function is widely used for testing optimization algorithms.
+    The function is usually evaluated on the hypercube x_i ∈ [-100, 100], for all i = 1, 2, ..., n.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bound for each variable (fixed to -100).
+    xu : float
+        Upper bound for each variable (fixed to 100).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Bentcigar function value for a single solution.
+
+    Notes
+    -----
+    -100 ≤ xi ≤ 100 for i = 1,…,n
+    Global minimum at f(0,...,0) = 0
+    """
+
+

+[docs]
+    def __init__(self, n_var: int):
+        """
+        Initialize the Bentcigar problem.
+
+        Parameters
+        ----------
+        n_var : int
+            Number of variables (dimensions) in the problem.
+        """
+        gen_type = GeneType.REAL  
+        xl = -100.0
+        xu = 100.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Bentcigar function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        a = pw(x[0], 2)  
+        b = pw(10, 6)    
+        sum_val = sum(pw(xi, 2) for xi in x[1:])  
+
+        fitness = a + (b * sum_val)
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/bohachevsky.html b/_modules/pycellga/problems/single_objective/continuous/bohachevsky.html new file mode 100644 index 0000000..6b9675a --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/bohachevsky.html @@ -0,0 +1,374 @@ + + + + + + pycellga.problems.single_objective.continuous.bohachevsky — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.bohachevsky
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.bohachevsky

+from numpy import cos, pi
+from mpmath import power as pw
+from typing import List
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+

+[docs]
+class Bohachevsky(AbstractProblem):
+    """
+    Bohachevsky function implementation for optimization problems.
+
+    The Bohachevsky function is widely used for testing optimization algorithms.
+    It is usually evaluated on the hypercube x_i ∈ [-15, 15], for all i = 1, 2, ..., n.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bound for each variable (fixed to -15).
+    xu : float
+        Upper bound for each variable (fixed to 15).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Bohachevsky function value for a single solution.
+
+    Notes
+    -----
+    -15 ≤ xi ≤ 15 for i = 1,…,n
+    Global minimum at f(0,...,0) = 0
+    """
+
+

+[docs]
+    def __init__(self, n_var: int):
+        """
+        Initialize the Bohachevsky problem.
+
+        Parameters
+        ----------
+        n_var : int
+            Number of variables (dimensions) in the problem.
+        """
+        gen_type = GeneType.REAL
+        xl = -15.0
+        xu = 15.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Bohachevsky function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        fitness = sum([
+            pw(x[i], 2) + (2 * pw(x[i + 1], 2)) 
+            - (0.3 * cos(3 * pi * x[i])) 
+            - (0.4 * cos(4 * pi * x[i + 1])) + 0.7
+            for i in range(len(x) - 1)
+        ])
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/chichinadze.html b/_modules/pycellga/problems/single_objective/continuous/chichinadze.html new file mode 100644 index 0000000..90913bd --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/chichinadze.html @@ -0,0 +1,371 @@ + + + + + + pycellga.problems.single_objective.continuous.chichinadze — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.chichinadze
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.chichinadze

+import numpy as np
+from typing import List
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+
+

+[docs]
+class Chichinadze(AbstractProblem):
+    """
+    Chichinadze function implementation for optimization problems.
+
+    The Chichinadze function is widely used for testing optimization algorithms.
+    It is usually evaluated on the hypercube x, y ∈ [-30, 30].
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (fixed to 2 for x and y).
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bound for the variables (fixed to -30).
+    xu : float
+        Upper bound for the variables (fixed to 30).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Chichinadze function value for a single solution.
+
+    Notes
+    -----
+    -30 ≤ x, y ≤ 30
+    Global minimum at f(5.90133, 0.5) = −43.3159
+    """
+
+

+[docs]
+    def __init__(self):
+        """
+        Initialize the Chichinadze problem.
+        """
+        gen_type = GeneType.REAL
+        n_var = 2  # Fixed to 2 for x and y
+        xl = -30.0
+        xu = 30.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Chichinadze function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        x_val, y_val = x[0], x[1]
+        term1 = x_val**2 - 12 * x_val + 11
+        term2 = 10 * np.cos(np.pi * x_val / 2)
+        term3 = 8 * np.sin(5 * np.pi * x_val)
+        term4 = (1.0 / np.sqrt(5)) * np.exp(-((y_val - 0.5)**2) / 2)
+        fitness = term1 + term2 + term3 - term4
+
+        return round(fitness, 4)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/dropwave.html b/_modules/pycellga/problems/single_objective/continuous/dropwave.html new file mode 100644 index 0000000..64db31a --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/dropwave.html @@ -0,0 +1,371 @@ + + + + + + pycellga.problems.single_objective.continuous.dropwave — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.dropwave
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.dropwave

+from problems.abstract_problem import AbstractProblem
+from numpy import power, cos, sqrt
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Dropwave(AbstractProblem):
+    """
+    Dropwave function for optimization problems.
+
+    The Dropwave function is a multimodal function commonly used as a performance test problem for optimization algorithms.
+    It is defined within the bounds -5.12 ≤ xi ≤ 5.12 for i = 1, 2, and has a global minimum at f(0, 0) = -1.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem (fixed to 2).
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bound for the variables (fixed to -5.12).
+    xu : float
+        Upper bound for the variables (fixed to 5.12).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Dropwave function value for a single solution.
+
+    Notes
+    -----
+    -5.12 ≤ xi ≤ 5.12 for i = 1, 2
+    Global minimum at f(0, 0) = -1
+    """
+
+

+[docs]
+    def __init__(self):
+        """
+        Initialize the Dropwave problem.
+        """
+        gen_type = GeneType.REAL
+        n_var = 2  # Fixed to 2 for x1 and x2
+        xl = -5.12
+        xu = 5.12
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Dropwave function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+        
+        x1, x2 = x
+        sqrts_sums = power(x1, 2) + power(x2, 2)
+        denominator = 0.5 * sqrts_sums + 2
+        fitness = -(1 + cos(12 * sqrt(sqrts_sums))) / denominator
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/fms.html b/_modules/pycellga/problems/single_objective/continuous/fms.html new file mode 100644 index 0000000..7d82df1 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/fms.html @@ -0,0 +1,375 @@ + + + + + + pycellga.problems.single_objective.continuous.fms — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.fms
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.fms

+from problems.abstract_problem import AbstractProblem
+from numpy import pi, sin
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Fms(AbstractProblem):
+    """
+    Fms function implementation for optimization problems.
+
+    The Fms function is used for testing optimization algorithms, specifically those dealing with frequency modulation sound.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem (fixed to 6).
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bound for the variables (fixed to -6.4).
+    xu : float
+        Upper bound for the variables (fixed to 6.35).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Fms function value for a single solution.
+    """
+
+

+[docs]
+    def __init__(self):
+        """
+        Initialize the Fms problem.
+        """
+        gen_type = GeneType.REAL
+        n_var = 6  # Fixed to 6 for [a1, w1, a2, w2, a3, w3]
+        xl = -6.4
+        xu = 6.35
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Fms function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+        
+        theta = (2.0 * pi) / 100.0
+        a1, w1, a2, w2, a3, w3 = x
+
+        def yzero(t):
+            return sin((5.0 * theta * t) - (1.5 * sin((4.8 * theta * t) + (2.0 * sin(4.9 * theta * t)))))
+
+        partial_fitness = 0.0
+        for k in range(101):
+            distance = (
+                a1 * sin((w1 * theta * k) - (a2 * sin((w2 * theta * k) + (a3 * sin(w3 * theta * k)))))
+                - yzero(k)
+            )
+            partial_fitness += distance ** 2
+
+        return round(partial_fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/griewank.html b/_modules/pycellga/problems/single_objective/continuous/griewank.html new file mode 100644 index 0000000..f578450 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/griewank.html @@ -0,0 +1,371 @@ + + + + + + pycellga.problems.single_objective.continuous.griewank — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.griewank
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.griewank

+from problems.abstract_problem import AbstractProblem
+import math
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Griewank(AbstractProblem):
+    """
+    Griewank function implementation for optimization problems.
+
+    The Griewank function is widely used for testing optimization algorithms.
+    It is usually evaluated on the hypercube xi ∈ [-600, 600], for all i = 1, 2, ..., n.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bound for the variables (fixed to -600).
+    xu : float
+        Upper bound for the variables (fixed to 600).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Griewank function value for a single solution.
+
+    Notes
+    -----
+    -600 ≤ xi ≤ 600 for i = 1,…,n
+    Global minimum at f(0,...,0) = 0
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 10):
+        """
+        Initialize the Griewank problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) in the problem, by default 10.
+        """
+        gen_type = GeneType.REAL
+        xl = -600.0
+        xu = 600.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Griewank function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        sum_sq = sum(xi ** 2 for xi in x)
+        prod_cos = math.prod(math.cos(xi / math.sqrt(i + 1)) for i, xi in enumerate(x))
+        fitness = 1 + sum_sq / 4000 - prod_cos
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/holzman.html b/_modules/pycellga/problems/single_objective/continuous/holzman.html new file mode 100644 index 0000000..0ae4ad2 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/holzman.html @@ -0,0 +1,369 @@ + + + + + + pycellga.problems.single_objective.continuous.holzman — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.holzman
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.holzman

+from problems.abstract_problem import AbstractProblem
+from mpmath import power as pw
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Holzman(AbstractProblem):
+    """
+    Holzman function implementation for optimization problems.
+
+    The Holzman function is widely used for testing optimization algorithms.
+    It is usually evaluated on the hypercube xi ∈ [-10, 10], for all i = 1, 2, ..., n.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bound for the variables (fixed to -10).
+    xu : float
+        Upper bound for the variables (fixed to 10).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Holzman function value for a single solution.
+
+    Notes
+    -----
+    -10 ≤ xi ≤ 10 for i = 1,…,n
+    Global minimum at f(0,...,0) = 0
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Holzman problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) in the problem, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -10.0
+        xu = 10.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Holzman function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        fitness = sum((i + 1) * pw(x[i], 4) for i in range(len(x)))
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/levy.html b/_modules/pycellga/problems/single_objective/continuous/levy.html new file mode 100644 index 0000000..4af469f --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/levy.html @@ -0,0 +1,392 @@ + + + + + + pycellga.problems.single_objective.continuous.levy — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.levy
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.levy

+from problems.abstract_problem import AbstractProblem
+import math
+from mpmath import power as pw
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Levy(AbstractProblem):
+    """
+    Levy function implementation for optimization problems.
+
+    The Levy function is widely used for testing optimization algorithms.
+    It evaluates inputs over the hypercube x_i ∈ [-10, 10].
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (REAL).
+    xl : float
+        Lower bounds for the variables, fixed to -10.
+    xu : float
+        Upper bounds for the variables, fixed to 10.
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Levy function value for a given solution.
+    evaluate(x: List[float], out: dict, *args, **kwargs) -> None
+        Pymoo-compatible evaluation method for batch processing.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Levy problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) for the problem, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -10.0
+        xu = 10.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Levy function value for a given solution.
+
+        Parameters
+        ----------
+        x : list
+            A list of float variables.
+
+        Returns
+        -------
+        float
+            The Levy function value.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+
+        fitness = 0.0
+        for i in range(self.n_var - 1):
+            term1 = pw(math.sin(3 * x[i] * math.pi), 2)
+            term2 = (pw((x[i] - 1), 2)) * (1 + pw(math.sin(3 * x[i + 1] * math.pi), 2))
+            term3 = (pw((x[i + 1] - 1), 2)) * (1 + pw(math.sin(2 * x[i + 1] * math.pi), 2))
+            fitness += term1 + term2 + term3
+
+        return round(fitness, 3)

+
+
+

+[docs]
+    def evaluate(self, x: List[float], out: dict, *args, **kwargs) -> None:
+        """
+        Evaluate method for compatibility with pymoo's framework.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/matyas.html b/_modules/pycellga/problems/single_objective/continuous/matyas.html new file mode 100644 index 0000000..0928fd6 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/matyas.html @@ -0,0 +1,368 @@ + + + + + + pycellga.problems.single_objective.continuous.matyas — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.matyas
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.matyas

+from problems.abstract_problem import AbstractProblem
+from mpmath import power as pw
+from common import GeneType
+
+

+[docs]
+class Matyas(AbstractProblem):
+    """
+    Matyas function implementation for optimization problems.
+
+    The Matyas function is commonly used to evaluate the performance of optimization algorithms.
+    It is a simple, continuous, convex function that has a global minimum at the origin.
+
+    Attributes
+    ----------
+    gen_type : GeneType
+        The type of genes used in the problem (fixed to REAL).
+    n_var : int
+        Number of variables (dimensions) in the problem (fixed to 2).
+    xl : float
+        Lower bound for the variables (fixed to -10).
+    xu : float
+        Upper bound for the variables (fixed to 10).
+
+    Methods
+    -------
+    f(x: list) -> float
+        Computes the Matyas function value for a given solution.
+    """
+
+

+[docs]
+    def __init__(self):
+        """
+        Initialize the Matyas problem.
+
+        This problem is defined for exactly 2 variables with bounds [-10, 10].
+
+        Parameters
+        ----------
+        None
+        """
+        gen_type = GeneType.REAL
+        n_var = 2
+        xl = -10.0
+        xu = 10.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x):
+        """
+        Compute the Matyas function value for a given solution.
+
+        Parameters
+        ----------
+        x : list of float
+            A list of float variables representing a point in the solution space.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) != 2:
+            raise ValueError("Matyas function is defined for exactly 2 variables.")
+
+        x1, x2 = x
+        fitness = 0.26 * (pw(x1, 2) + pw(x2, 2)) - 0.48 * x1 * x2
+        return round(fitness, 2)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/pow.html b/_modules/pycellga/problems/single_objective/continuous/pow.html new file mode 100644 index 0000000..d939868 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/pow.html @@ -0,0 +1,372 @@ + + + + + + pycellga.problems.single_objective.continuous.pow — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.pow
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.pow

+from problems.abstract_problem import AbstractProblem
+from mpmath import power as pw
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Pow(AbstractProblem):
+    """
+    Pow function implementation for optimization problems.
+
+    The Pow function is typically used for testing optimization algorithms.
+    It is evaluated on the hypercube x_i ∈ [-5.0, 15.0] with the goal of reaching 
+    the global minimum at f(5, 7, 9, 3, 2) = 0.
+
+    Attributes
+    ----------
+    gen_type : GeneType
+        The type of genes used in the problem (REAL).
+    n_var : int
+        The number of design variables.
+    xl : float
+        The lower bound for the variables (-5.0).
+    xu : float
+        The upper bound for the variables (15.0).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Pow function value for a given solution.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 5):
+        """
+        Initialize the Pow problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            The number of variables (dimensions) in the problem, by default 5.
+        """
+        gen_type = GeneType.REAL
+        xl = -5.0
+        xu = 15.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Pow function value for a given solution.
+
+        Parameters
+        ----------
+        x : list of float
+            A list of float variables representing a point in the solution space.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+
+        # Define the target values
+        target = [5, 7, 9, 3, 2]
+
+        # Compute the fitness as the sum of squared differences
+        fitness = sum(pw(xi - ti, 2) for xi, ti in zip(x, target))
+        return round(fitness, 2)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/powell.html b/_modules/pycellga/problems/single_objective/continuous/powell.html new file mode 100644 index 0000000..fdf1da4 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/powell.html @@ -0,0 +1,375 @@ + + + + + + pycellga.problems.single_objective.continuous.powell — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.powell
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.powell

+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+from mpmath import power as pw
+from typing import List
+
+

+[docs]
+class Powell(AbstractProblem):
+    """
+    Powell function implementation for optimization problems.
+
+    The Powell function is widely used for testing optimization algorithms.
+    It is typically evaluated on the hypercube x_i ∈ [-4, 5], for all i = 1, 2, ..., n.
+    
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (REAL for this implementation).
+    xl : float
+        Lower bound for the variables (fixed to -4).
+    xu : float
+        Upper bound for the variables (fixed to 5).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Powell function value for a given solution.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 4):
+        """
+        Initialize the Powell problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) in the problem, by default 4.
+        """
+        gen_type = GeneType.REAL
+        xl = -4.0
+        xu = 5.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Powell function value for a given solution.
+
+        Parameters
+        ----------
+        x : list of float
+            A list of float variables representing a point in the solution space.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) % 4 != 0:
+            raise ValueError("Powell function requires the number of variables to be a multiple of 4.")
+
+        fitness = 0.0
+        d = len(x) // 4
+        
+        for i in range(d):
+            a = pw(x[4 * i] + 10 * x[4 * i + 1], 2)
+            b = pw(x[4 * i + 2] - x[4 * i + 3], 2)
+            c = pw(x[4 * i + 1] - 2 * x[4 * i + 2], 4)
+            e = pw(x[4 * i] - x[4 * i + 3], 4)
+            fitness += a + 5 * b + c + 10 * e
+
+        return round(fitness, 1)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/rastrigin.html b/_modules/pycellga/problems/single_objective/continuous/rastrigin.html new file mode 100644 index 0000000..064067c --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/rastrigin.html @@ -0,0 +1,367 @@ + + + + + + pycellga.problems.single_objective.continuous.rastrigin — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.rastrigin
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.rastrigin

+from numpy import cos, pi
+from typing import List
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+

+[docs]
+class Rastrigin(AbstractProblem):
+    """
+    Rastrigin function implementation for optimization problems.
+
+    The Rastrigin function is widely used for testing optimization algorithms.
+    It is typically evaluated on the hypercube x_i ∈ [-5.12, 5.12], for all i = 1, 2, ..., n.
+
+    Attributes
+    ----------
+    gen_type : GeneType
+        The type of genes used in the problem, set to REAL.
+    n_var : int
+        The number of variables (dimensions) in the problem.
+    xl : float
+        The lower bound for each variable, set to -5.12.
+    xu : float
+        The upper bound for each variable, set to 5.12.
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Computes the Rastrigin function value for a given solution.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Rastrigin problem with the specified number of variables.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            The number of design variables (dimensions), by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -5.12
+        xu = 5.12
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Calculate the Rastrigin function value for a given list of variables.
+
+        Parameters
+        ----------
+        x : list
+            A list of float variables.
+
+        Returns
+        -------
+        float
+            The computed Rastrigin function value.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+
+        A = 10.0
+        fitness = (A * self.n_var) + sum([(xi ** 2) - (A * cos(2 * pi * xi)) for xi in x])
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/rosenbrock.html b/_modules/pycellga/problems/single_objective/continuous/rosenbrock.html new file mode 100644 index 0000000..53948b5 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/rosenbrock.html @@ -0,0 +1,367 @@ + + + + + + pycellga.problems.single_objective.continuous.rosenbrock — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.rosenbrock
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.rosenbrock

+from problems.abstract_problem import AbstractProblem
+from mpmath import power as pw
+from common import GeneType
+from typing import List
+
+
+

+[docs]
+class Rosenbrock(AbstractProblem):
+    """
+    Rosenbrock function implementation for optimization problems.
+
+    The Rosenbrock function is widely used for testing optimization algorithms.
+    The function is usually evaluated on the hypercube x_i ∈ [-5, 10], for all i = 1, 2, ..., n.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bounds for the variables (fixed to -5).
+    xu : float
+        Upper bounds for the variables (fixed to 10).
+
+    Methods
+    -------
+    f(x: List[float]) -> float
+        Compute the Rosenbrock function value for a single solution.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Rosenbrock problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) in the problem, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -5.0
+        xu = 10.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Rosenbrock function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+
+        fitness = sum([(100 * pw((x[i + 1] - pw(x[i], 2)), 2)) + pw((1 - x[i]), 2) for i in range(self.n_var - 1)])
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/rothellipsoid.html b/_modules/pycellga/problems/single_objective/continuous/rothellipsoid.html new file mode 100644 index 0000000..2f363bf --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/rothellipsoid.html @@ -0,0 +1,384 @@ + + + + + + pycellga.problems.single_objective.continuous.rothellipsoid — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.rothellipsoid
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.rothellipsoid

+from problems.abstract_problem import AbstractProblem
+from mpmath import power as pw
+from common import GeneType
+
+

+[docs]
+class Rothellipsoid(AbstractProblem):
+    """
+    Rotated Hyper-Ellipsoid function implementation for optimization problems.
+
+    This function is widely used for testing optimization algorithms.
+    It is usually evaluated on the hypercube x_i ∈ [-100, 100], for all i = 1, 2, ..., n.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) for the problem.
+    gen_type : GeneType
+        The type of genes used in the problem, set to REAL.
+    xl : float
+        Lower bound for the variables, set to -100.
+    xu : float
+        Upper bound for the variables, set to 100.
+
+    Methods
+    -------
+    f(x: list) -> float
+        Compute the Rotated Hyper-Ellipsoid function value for a given list of variables.
+    evaluate(x, out, *args, **kwargs)
+        Computes the fitness value for pymoo compatibility.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 3):
+        """
+        Initialize the Rothellipsoid problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) for the problem, by default 3.
+        """
+        gen_type = GeneType.REAL
+        xl = -100.0
+        xu = 100.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x):
+        """
+        Compute the Rotated Hyper-Ellipsoid function value for a given solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        fitness = sum((i + 1) * pw(x[i], 2) for i in range(len(x)))
+        return round(fitness, 3)

+
+
+

+[docs]
+    def evaluate(self, x, out, *args, **kwargs):
+        """
+        Evaluate the function for pymoo compatibility.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the computed fitness value.
+
+        Notes
+        -----
+        Stores the computed fitness value in `out["F"]`.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/schaffer.html b/_modules/pycellga/problems/single_objective/continuous/schaffer.html new file mode 100644 index 0000000..0ee1703 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/schaffer.html @@ -0,0 +1,370 @@ + + + + + + pycellga.problems.single_objective.continuous.schaffer — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.schaffer
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.schaffer

+import numpy as np
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+

+[docs]
+class Schaffer(AbstractProblem):
+    """
+    Modified Schaffer function #1 for optimization problems.
+
+    This class implements the Schaffer's function, a common benchmark problem for optimization algorithms.
+    The function is defined over a multidimensional input and is used to test the performance of optimization methods.
+
+    Attributes
+    ----------
+    gen_type : GeneType
+        The type of gene, set to REAL.
+    n_var : int
+        The number of design variables.
+    xl : float
+        The lower bound for the design variables, set to -100.
+    xu : float
+        The upper bound for the design variables, set to 100.
+
+    Methods
+    -------
+    f(x: list) -> float
+        Computes the Schaffer's function value for a given list of variables.
+    evaluate(x, out, *args, **kwargs)
+        Wrapper for pymoo compatibility to calculate the fitness value.
+    """
+
+

+[docs]
+    def __init__(self, n_var=2):
+        """
+        Initialize the Schaffer's problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            The number of design variables, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -100.0
+        xu = 100.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x):
+        """
+        Compute the Schaffer's function value for a given solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The calculated fitness value.
+        """
+        fitness = 0.0
+        for i in range(len(x) - 1):
+            xi = x[i]
+            xi1 = x[i + 1]
+            term1 = np.sin(xi**2 + xi1**2)**2
+            term2 = 1 + 0.001 * (xi**2 + xi1**2)
+            fitness += 0.5 + (term1 - 0.5)**2 / term2**2
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/schaffer2.html b/_modules/pycellga/problems/single_objective/continuous/schaffer2.html new file mode 100644 index 0000000..f18eb91 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/schaffer2.html @@ -0,0 +1,369 @@ + + + + + + pycellga.problems.single_objective.continuous.schaffer2 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.schaffer2
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.schaffer2

+import numpy as np
+from numpy import power as pw
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+

+[docs]
+class Schaffer2(AbstractProblem):
+    """
+    Modified Schaffer function #2 implementation for optimization problems.
+
+    The Modified Schaffer function #2 is widely used for testing optimization algorithms.
+    The function is evaluated on the hypercube x_i ∈ [-100, 100], for all i = 1, 2, ..., n.
+
+    Attributes
+    ----------
+    n_var : int
+        The number of variables (dimensions) for the problem.
+    gen_type : GeneType
+        Type of genes used in the problem, fixed to REAL.
+    xl : float
+        Lower bounds for the variables, fixed to -100.
+    xu : float
+        Upper bounds for the variables, fixed to 100.
+
+    Methods
+    -------
+    f(x: list) -> float
+        Compute the Modified Schaffer function #2 value for a single solution.
+    evaluate(x, out, *args, **kwargs)
+        Compute the fitness value(s) for pymoo's optimization framework.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Modified Schaffer function #2.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) for the problem, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -100.0
+        xu = 100.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: list) -> float:
+        """
+        Compute the Modified Schaffer function #2 value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        fitness = 0.0
+        for i in range(len(x) - 1):
+            term1 = np.sin(pw(x[i], 2) - pw(x[i + 1], 2)) ** 2
+            term2 = (1 + 0.001 * (pw(x[i], 2) + pw(x[i + 1], 2))) ** 2
+            fitness += 0.5 + ((term1 - 0.5) / term2)
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/schwefel.html b/_modules/pycellga/problems/single_objective/continuous/schwefel.html new file mode 100644 index 0000000..3464b3f --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/schwefel.html @@ -0,0 +1,369 @@ + + + + + + pycellga.problems.single_objective.continuous.schwefel — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.schwefel
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.schwefel

+from numpy import sin, sqrt, abs
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+

+[docs]
+class Schwefel(AbstractProblem):
+    """
+    Schwefel function implementation for optimization problems.
+
+    The Schwefel function is commonly used for testing optimization algorithms.
+    It is evaluated on the range [-500, 500] for each variable and has a global minimum
+    at f(420.9687,...,420.9687) = 0.
+
+    Attributes
+    ----------
+    n_var : int
+        The number of variables (dimensions) for the problem.
+    gen_type : GeneType
+        The type of genes used in the problem, fixed to REAL.
+    xl : float
+        The lower bounds for the variables, fixed to -500.
+    xu : float
+        The upper bounds for the variables, fixed to 500.
+
+    Methods
+    -------
+    f(x: list) -> float
+        Compute the Schwefel function value for a single solution.
+
+    Notes
+    -----
+    -500 ≤ xi ≤ 500 for i = 1,…,n
+    Global minimum at f(420.9687,...,420.9687) = 0
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Schwefel function with the specified number of variables.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            The number of variables (dimensions) for the problem, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -500.0
+        xu = 500.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: list) -> float:
+        """
+        Compute the Schwefel function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        d = len(x)
+        fitness = sum(xi * sin(sqrt(abs(xi))) for xi in x)
+        return round((418.9829 * d) - fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/sphere.html b/_modules/pycellga/problems/single_objective/continuous/sphere.html new file mode 100644 index 0000000..74b6581 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/sphere.html @@ -0,0 +1,369 @@ + + + + + + pycellga.problems.single_objective.continuous.sphere — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.sphere
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.sphere

+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+

+[docs]
+class Sphere(AbstractProblem):
+    """
+    Sphere function implementation for optimization problems.
+
+    The Sphere function is a simple and commonly used benchmark for optimization algorithms.
+    It is defined on a hypercube where each variable typically lies within [-5.12, 5.12].
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bounds for the variables (fixed to -5.12).
+    xu : float
+        Upper bounds for the variables (fixed to 5.12).
+
+    Methods
+    -------
+    f(x: list) -> float
+        Compute the Sphere function value for a single solution.
+
+    Notes
+    -----
+    -5.12 ≤ xi ≤ 5.12 for i = 1,…,n
+    Global minimum at f(0,...,0) = 0
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 10):
+        """
+        Initialize the Sphere function with the specified number of variables.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) in the problem, by default 10.
+        """
+        gen_type = GeneType.REAL
+        xl = -5.12
+        xu = 5.12
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: list) -> float:
+        """
+        Compute the Sphere function value for a single solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+
+        fitness = sum(xi**2 for xi in x)
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/styblinskitang.html b/_modules/pycellga/problems/single_objective/continuous/styblinskitang.html new file mode 100644 index 0000000..4e64c46 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/styblinskitang.html @@ -0,0 +1,388 @@ + + + + + + pycellga.problems.single_objective.continuous.styblinskitang — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.styblinskitang
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.styblinskitang

+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+

+[docs]
+class StyblinskiTang(AbstractProblem):
+    """
+    Styblinski-Tang function implementation for optimization problems.
+
+    The Styblinski-Tang function is commonly used to test optimization algorithms. 
+    It is defined over the range [-5, 5] for each variable and has a global minimum.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bounds for the variables (fixed to -5).
+    xu : float
+        Upper bounds for the variables (fixed to 5).
+
+    Methods
+    -------
+    f(x: list) -> float
+        Compute the Styblinski-Tang function value for a given solution.
+
+    Notes
+    -----
+    -5 ≤ xi ≤ 5 for i = 1,…,n
+    Global minimum at f(-2.903534, ..., -2.903534) ≈ -39.16599 * n_var
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Styblinski-Tang function with the specified number of variables.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) in the problem, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -5.0
+        xu = 5.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: list) -> float:
+        """
+        Compute the Styblinski-Tang function value for a given solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+
+        fitness = sum(xi**4 - 16 * xi**2 + 5 * xi for xi in x) / self.n_var
+        return round(fitness, 3)

+
+
+

+[docs]
+    def evaluate(self, x, out, *args, **kwargs):
+        """
+        Evaluate function for compatibility with pymoo's optimizer.
+
+        This method wraps the `f` method and allows pymoo to handle batch evaluations
+        by storing the computed fitness values in the output dictionary.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/sumofdifferentpowers.html b/_modules/pycellga/problems/single_objective/continuous/sumofdifferentpowers.html new file mode 100644 index 0000000..c74d868 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/sumofdifferentpowers.html @@ -0,0 +1,386 @@ + + + + + + pycellga.problems.single_objective.continuous.sumofdifferentpowers — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.sumofdifferentpowers
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.sumofdifferentpowers

+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+import numpy as np
+from mpmath import power as pw
+

+[docs]
+class Sumofdifferentpowers(AbstractProblem):
+    """
+    Sum of Different Powers function implementation for optimization problems.
+
+    The Sum of Different Powers function is commonly used to test optimization algorithms.
+    It is defined over the range [-1, 1] for each variable, with a global minimum of 0 at the origin.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (fixed to REAL).
+    xl : float
+        Lower bounds for the variables (fixed to -1).
+    xu : float
+        Upper bounds for the variables (fixed to 1).
+
+    Methods
+    -------
+    f(x: list) -> float
+        Compute the Sum of Different Powers function value for a given solution.
+
+    Notes
+    -----
+    -1 ≤ xi ≤ 1 for all i.
+    Global minimum at f(0,...,0) = 0.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Sum of Different Powers problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) in the problem, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -1.0
+        xu = 1.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: list) -> float:
+        """
+        Compute the Sum of Different Powers function value for a given solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+
+        fitness = sum(pw(np.abs(xi), i + 1) for i, xi in enumerate(x))
+        return round(fitness, 3)

+
+
+

+[docs]
+    def evaluate(self, x, out, *args, **kwargs):
+        """
+        Evaluate function for compatibility with pymoo's optimizer.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/threehumps.html b/_modules/pycellga/problems/single_objective/continuous/threehumps.html new file mode 100644 index 0000000..0c6b069 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/threehumps.html @@ -0,0 +1,379 @@ + + + + + + pycellga.problems.single_objective.continuous.threehumps — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.threehumps
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.threehumps

+from mpmath import power as pw
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+
+

+[docs]
+class Threehumps(AbstractProblem):
+    """
+    Three Hump Camel function implementation for optimization problems.
+
+    The Three Hump Camel function is commonly used for testing optimization algorithms.
+    It is defined for two variables within the bounds [-5, 5].
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) for the problem, fixed to 2.
+    gen_type : GeneType
+        Type of genes used in the problem (REAL).
+    xl : float
+        Lower bounds for the variables, fixed to -5.
+    xu : float
+        Upper bounds for the variables, fixed to 5.
+
+    Methods
+    -------
+    f(x: list) -> float
+        Compute the Three Hump Camel function value for a given solution.
+    """
+
+

+[docs]
+    def __init__(self):
+        """
+        Initialize the Three Hump Camel problem.
+        """
+        gen_type = GeneType.REAL
+        n_var = 2
+        xl = -5.0
+        xu = 5.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: list) -> float:
+        """
+        Compute the Three Hump Camel function value for a given solution.
+
+        Parameters
+        ----------
+        x : list or numpy.ndarray
+            Array of input variables.
+
+        Returns
+        -------
+        float
+            The computed fitness value for the given solution.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+        
+        x1, x2 = x
+        fitness = 2 * pw(x1, 2) - 1.05 * pw(x1, 4) + (pw(x1, 6) / 6) + x1 * x2 + pw(x2, 2)
+        return round(fitness, 6)

+
+
+

+[docs]
+    def evaluate(self, x, out, *args, **kwargs):
+        """
+        Evaluate method for compatibility with pymoo's framework.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/zakharov.html b/_modules/pycellga/problems/single_objective/continuous/zakharov.html new file mode 100644 index 0000000..c9947a0 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/zakharov.html @@ -0,0 +1,388 @@ + + + + + + pycellga.problems.single_objective.continuous.zakharov — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.zakharov
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.zakharov

+from mpmath import power as pw
+from typing import List
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+
+

+[docs]
+class Zakharov(AbstractProblem):
+    """
+    Zakharov function implementation for optimization problems.
+
+    The Zakharov function is widely used for testing optimization algorithms.
+    It is evaluated on the hypercube x_i ∈ [-5, 10] for all variables.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (REAL).
+    xl : float
+        Lower bounds for the variables, fixed to -5.
+    xu : float
+        Upper bounds for the variables, fixed to 10.
+
+    Methods
+    -------
+    f(x: list) -> float
+        Compute the Zakharov function value for a given solution.
+    evaluate(x: list, out: dict, *args, **kwargs) -> None
+        Pymoo-compatible evaluation method for batch processing.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Zakharov problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) for the problem, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -5.0
+        xu = 10.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Zakharov function value for a given solution.
+
+        Parameters
+        ----------
+        x : list
+            A list of float variables.
+
+        Returns
+        -------
+        float
+            The Zakharov function value.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+
+        fitness1 = sum(pw(xi, 2) for xi in x)
+        fitness2 = pw(sum(0.5 * (i + 1) * xi for i, xi in enumerate(x)), 2)
+        fitness3 = pw(sum(0.5 * (i + 1) * xi for i, xi in enumerate(x)), 4)
+        fitness = fitness1 + fitness2 + fitness3
+        return round(fitness, 3)

+
+
+

+[docs]
+    def evaluate(self, x: List[float], out: dict, *args, **kwargs) -> None:
+        """
+        Evaluate method for compatibility with pymoo's framework.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/continuous/zettle.html b/_modules/pycellga/problems/single_objective/continuous/zettle.html new file mode 100644 index 0000000..4a3544a --- /dev/null +++ b/_modules/pycellga/problems/single_objective/continuous/zettle.html @@ -0,0 +1,388 @@ + + + + + + pycellga.problems.single_objective.continuous.zettle — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.continuous.zettle
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.continuous.zettle

+from mpmath import power as pw
+from typing import List
+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+
+

+[docs]
+class Zettle(AbstractProblem):
+    """
+    Zettle function implementation for optimization problems.
+
+    The Zettle function is widely used for testing optimization algorithms.
+    It is typically evaluated on the hypercube x_i ∈ [-5, 5].
+
+    Attributes
+    ----------
+    n_var : int
+        Number of variables (dimensions) in the problem.
+    gen_type : GeneType
+        Type of genes used in the problem (REAL).
+    xl : float
+        Lower bounds for the variables, fixed to -5.
+    xu : float
+        Upper bounds for the variables, fixed to 5.
+
+    Methods
+    -------
+    f(x: list) -> float
+        Compute the Zettle function value for a given solution.
+    evaluate(x: list, out: dict, *args, **kwargs) -> None
+        Pymoo-compatible evaluation method for batch processing.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 2):
+        """
+        Initialize the Zettle problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of variables (dimensions) for the problem, by default 2.
+        """
+        gen_type = GeneType.REAL
+        xl = -5.0
+        xu = 5.0
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[float]) -> float:
+        """
+        Compute the Zettle function value for a given solution.
+
+        Parameters
+        ----------
+        x : list
+            A list of float variables.
+
+        Returns
+        -------
+        float
+            The Zettle function value, rounded to six decimal places.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+        
+        fitness = 0.0
+        for i in range(len(x) - 1):
+            fitness += pw((pw(x[i], 2) + pw(x[i + 1], 2)) - 2 * x[i], 2) + 0.25 * x[i]
+
+        return round(fitness, 6)

+
+
+

+[docs]
+    def evaluate(self, x: List[float], out: dict, *args, **kwargs) -> None:
+        """
+        Evaluate method for compatibility with pymoo's framework.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/binary/count_sat.html b/_modules/pycellga/problems/single_objective/discrete/binary/count_sat.html new file mode 100644 index 0000000..49d84e1 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/binary/count_sat.html @@ -0,0 +1,394 @@ + + + + + + pycellga.problems.single_objective.discrete.binary.count_sat — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.binary.count_sat
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.binary.count_sat

+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+from typing import List
+
+

+[docs]
+class CountSat(AbstractProblem):
+    """
+    CountSat function implementation for optimization problems.
+
+    The CountSat function is used for testing optimization algorithms, particularly those involving satisfiability problems.
+
+    Attributes
+    ----------
+    n_var : int
+        The number of variables (chromosome length) for the problem.
+    gen_type : GeneType
+        The type of genes used in the problem, set to BINARY.
+    xl : int
+        Lower bounds for binary variables, fixed to 0.
+    xu : int
+        Upper bounds for binary variables, fixed to 1.
+
+    Methods
+    -------
+    f(x: List[int]) -> float
+        Calculates the CountSat function value for a given list of binary variables.
+    evaluate(x: List[int], out: dict, *args, **kwargs) -> None
+        Pymoo-compatible evaluation method for batch processing.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 20):
+        """
+        Initialize the CountSat problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of binary variables (chromosome length) for the problem, by default 20.
+        """
+        gen_type = GeneType.BINARY
+        xl = 0  # Lower bound for binary variables
+        xu = 1  # Upper bound for binary variables
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[int]) -> float:
+        """
+        Calculate the CountSat function value for a given list of binary variables.
+
+        Parameters
+        ----------
+        x : list
+            A list of binary variables.
+
+        Returns
+        -------
+        float
+            The normalized CountSat function value.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+        
+        total_ones = sum(1 for i in x if i == 1)
+        variables = len(x)
+
+        # Calculate the fitness based on the CountSat formula
+        fitness = (
+            total_ones
+            + (variables * (variables - 1) * (variables - 2))
+            - ((variables - 2) * total_ones * (total_ones - 1))
+            + (total_ones * (total_ones - 1) * (total_ones - 2))
+        )
+
+        # Normalize the fitness value
+        fitness_normalized = fitness / 6860
+        return round(fitness_normalized, 3)

+
+
+

+[docs]
+    def evaluate(self, x: List[int], out: dict, *args, **kwargs) -> None:
+        """
+        Evaluate function for compatibility with pymoo's optimizer.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/binary/ecc.html b/_modules/pycellga/problems/single_objective/discrete/binary/ecc.html new file mode 100644 index 0000000..f6586fc --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/binary/ecc.html @@ -0,0 +1,401 @@ + + + + + + pycellga.problems.single_objective.discrete.binary.ecc — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.binary.ecc
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.binary.ecc

+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+from typing import List
+
+

+[docs]
+class Ecc(AbstractProblem):
+    """
+    Error Correcting Codes Design Problem (ECC) function implementation for optimization problems.
+
+    The ECC function is used for testing optimization algorithms, particularly those involving 
+    error-correcting codes.
+
+    Attributes
+    ----------
+    n_var : int
+        Number of binary variables, typically 144.
+    gen_type : GeneType
+        The type of genes used in the problem, set to BINARY.
+    xl : int
+        Lower bounds for binary variables, fixed to 0.
+    xu : int
+        Upper bounds for binary variables, fixed to 1.
+
+    Methods
+    -------
+    f(x: List[int]) -> float
+        Calculates the ECC function value for a given list of binary variables.
+    evaluate(x: List[int], out: dict, *args, **kwargs) -> None
+        Pymoo-compatible evaluation method for batch processing.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 144):
+        """
+        Initialize the ECC problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of binary variables for the problem, by default 144.
+        """
+        gen_type = GeneType.BINARY
+        xl = 0  # Lower bound for binary variables
+        xu = 1  # Upper bound for binary variables
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[int]) -> float:
+        """
+        Calculate the ECC function value for a given list of binary variables.
+
+        Parameters
+        ----------
+        x : list
+            A list of binary variables.
+
+        Returns
+        -------
+        float
+            The ECC function value, rounded to four decimal places.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+        
+        individual_length = 12  # Length of individual code segments
+        half_code = self.n_var // individual_length  # Number of code segments
+        partial_fitness = 0.0  # Accumulated partial fitness value
+
+        for i in range(half_code):
+            for j in range(i + 1, half_code):  # Avoid double-counting pairs
+                hamming = sum(
+                    x[i * individual_length + k] != x[j * individual_length + k]
+                    for k in range(individual_length)
+                )
+
+                if 0 < hamming < individual_length:
+                    partial_fitness += (
+                        1.0 / (hamming * hamming)
+                        + 1.0 / ((individual_length - hamming) * (individual_length - hamming))
+                    )
+
+        # Calculate final fitness value
+        fitness = 1.0 / (2 * partial_fitness) if partial_fitness != 0 else 0.0
+        return round(fitness, 4)

+
+
+

+[docs]
+    def evaluate(self, x: List[int], out: dict, *args, **kwargs) -> None:
+        """
+        Evaluate function for compatibility with pymoo's optimizer.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/binary/fms.html b/_modules/pycellga/problems/single_objective/discrete/binary/fms.html new file mode 100644 index 0000000..101c7c0 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/binary/fms.html @@ -0,0 +1,407 @@ + + + + + + pycellga.problems.single_objective.discrete.binary.fms — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.binary.fms
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.binary.fms

+from problems.abstract_problem import AbstractProblem
+from numpy import pi, sin, random
+from common import GeneType
+from typing import List
+
+

+[docs]
+class Fms(AbstractProblem):
+    """
+    Frequency Modulation Sound (FMS) function implementation for optimization problems.
+
+    The FMS function is used for testing optimization algorithms, particularly those involving frequency modulation sound.
+
+    Attributes
+    ----------
+    n_var : int
+        The number of binary variables for the problem, typically 192.
+    gen_type : GeneType
+        The type of genes used in the problem, set to BINARY.
+    xl : int
+        Lower bounds for binary variables, fixed to 0.
+    xu : int
+        Upper bounds for binary variables, fixed to 1.
+
+    Methods
+    -------
+    f(x: List[int]) -> float
+        Calculates the FMS function value for a given list of binary variables.
+    evaluate(x: List[int], out: dict, *args, **kwargs) -> None
+        Pymoo-compatible evaluation method for batch processing.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 192):
+        """
+        Initialize the FMS problem.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of binary variables for the problem, by default 192.
+        """
+        gen_type = GeneType.BINARY
+        xl = 0  # Lower bound for binary variables
+        xu = 1  # Upper bound for binary variables
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[int]) -> float:
+        """
+        Calculate the FMS function value for a given list of binary variables.
+
+        Parameters
+        ----------
+        x : list
+            A list of binary variables.
+
+        Returns
+        -------
+        float
+            The FMS function value.
+        """
+        if len(x) != self.n_var:
+            raise ValueError(f"Input must have exactly {self.n_var} variables.")
+
+        theta = (2.0 * pi) / 100.0
+        random.seed(100)
+
+        # Decode binary variables into continuous parameters
+        def decode_segment(segment):
+            value = 0
+            for bit in segment:
+                value = (value << 1) | bit
+            return -6.4 + (12.75 * (value / 4294967295.0))
+
+        a1 = decode_segment(x[:32])
+        w1 = decode_segment(x[32:64])
+        a2 = decode_segment(x[64:96])
+        w2 = decode_segment(x[96:128])
+        a3 = decode_segment(x[128:160])
+        w3 = decode_segment(x[160:192])
+
+        # Generate target signal
+        target = [sin((5.0 * theta * i) - (1.5 * sin((4.8 * theta * i) + (2.0 * sin(4.9 * theta * i))))) for i in range(101)]
+
+        # Generate predicted signal
+        y = [a1 * sin((w1 * theta * j) - (a2 * sin((w2 * theta * j) + (a3 * sin(w3 * theta * j))))) for j in range(101)]
+
+        # Compute mean squared error
+        fitness = sum((target[k] - y[k]) ** 2 for k in range(101))
+        return round(fitness, 3)

+
+
+

+[docs]
+    def evaluate(self, x: List[int], out: dict, *args, **kwargs) -> None:
+        """
+        Evaluate function for compatibility with pymoo's optimizer.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/binary/maxcut100.html b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut100.html new file mode 100644 index 0000000..15b7949 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut100.html @@ -0,0 +1,571 @@ + + + + + + pycellga.problems.single_objective.discrete.binary.maxcut100 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.binary.maxcut100
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.binary.maxcut100

+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+
+
+

+[docs]
+class Maxcut100(AbstractProblem):
+    """
+    A class to represent the Maximum Cut (MAXCUT) problem for 100 nodes.
+
+    Attributes
+    ----------
+    problema : list of list of float
+        A matrix representing the weights between nodes in the MAXCUT problem.
+    """
+
+

+[docs]
+    def __init__(self):
+        
+        self.problema = [
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 10.000000, 0.000000, 10.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 10.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 10.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 10.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 10.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 10.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 10.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
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+        ]
+
+        
+        n_var = 100  
+        xl = 0
+        xu = 1
+        gen_type = GeneType.BINARY
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x):
+        """
+        Fitness function for the Maxcut problem.
+        Calculates the cut value of the given binary solution vector `x`.
+
+        Parameters
+        ----------
+        x : list of int
+            Binary vector representing the solution.
+
+        Returns
+        -------
+        float
+            The fitness value (cut value).
+        """
+        fitness = 0.0
+        n = len(self.problema)
+        
+        for i in range(n):
+            for j in range(i + 1, n):
+                if x[i] != x[j]:  # Nodes are in different subsets
+                    fitness += self.problema[i][j]
+
+        return fitness

+
+
+

+[docs]
+    def evaluate(self, x, out, *args, **kwargs):
+        """
+        Evaluates the Maxcut problem for a given solution `x`.
+
+        Parameters
+        ----------
+        x : ndarray
+            Decision variable matrix (each row is a solution vector).
+        out : dict
+            Output dictionary where results will be stored.
+        """
+        # Fitness değerlerini hesapla ve çıktıya ekle
+        out["F"] = [self.f(ind) for ind in x]

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_01.html b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_01.html new file mode 100644 index 0000000..7f3fe09 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_01.html @@ -0,0 +1,421 @@ + + + + + + pycellga.problems.single_objective.discrete.binary.maxcut20_01 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.binary.maxcut20_01
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.binary.maxcut20_01

+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+from typing import List
+
+

+[docs]
+class Maxcut20_01(AbstractProblem):
+    """
+    Maximum Cut (MAXCUT) function implementation for optimization problems.
+
+    The MAXCUT function evaluates the fitness of a binary partition of nodes based on edge weights.
+    It is used to test optimization algorithms, particularly for maximum cut problems.
+
+    Attributes
+    ----------
+    problema : list of list of float
+        Adjacency matrix representing edge weights between nodes.
+
+    Methods
+    -------
+    f(x: list) -> float
+        Calculates the MAXCUT function value for a given list of binary variables.
+    evaluate(x: list, out: dict) -> None
+        Pymoo-compatible evaluation method for batch processing.
+    """
+
+

+[docs]
+    def __init__(self):
+        """
+        Initialize the MAXCUT problem with binary variables and adjacency matrix.
+        """
+        n_var = 20  # Number of binary variables (nodes)
+        xl = 0  # Lower bound for binary variables
+        xu = 1  # Upper bound for binary variables
+        gen_type = GeneType.BINARY
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)
+
+        # Define adjacency matrix (20x20 matrix of edge weights)
+        self.problema = [
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.359902,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.313702, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.848267, 0.000000, 0.000000, 0.000000, 0.287508, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.117489,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.190953, 0.000000, 0.000000, 0.000000, 0.000000, 0.916311],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.848267, 0.000000, 0.000000, 0.000000, 0.084579, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.721013, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.084579, 0.000000, 0.000000, 0.000000, 0.000000, 0.863363,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.032054],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.764415, 0.000000, 0.495863, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.287508, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.109939, 0.000000, 0.000000, 0.000000, 0.167750, 0.000000, 0.000000, 0.000000],
+            [0.359902, 0.000000, 0.117489, 0.000000, 0.000000, 0.863363, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.928091, 0.000000, 0.000000, 0.118362, 0.000000, 0.000000, 0.969750],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.652776, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.764415, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.424253, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.109939, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.495863, 0.000000, 0.000000, 0.928091,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.190953, 0.000000, 0.721013, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.252964, 0.936165, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.652776, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.313702, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.167750, 0.118362,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.424253, 0.000000, 0.000000, 0.252964, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.936165, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
+            [0.000000, 0.000000, 0.916311, 0.000000, 0.000000, 0.032054, 0.000000, 0.000000, 0.000000, 0.969750,
+                0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000]
+        ]

+
+
+

+[docs]
+    def f(self, x: List[int]) -> float:
+        """
+        Calculate the MAXCUT function value for a given list of binary variables.
+
+        Parameters
+        ----------
+        x : list
+            A list of binary variables representing node partitions.
+
+        Returns
+        -------
+        float
+            The MAXCUT function value representing the total weight of edges cut by the partition.
+        """
+        fitness = 0.0
+        cols = len(self.problema)
+
+        for i in range(cols - 1):
+            for j in range(i + 1, cols):
+                if x[i] != x[j]:  # Nodes are in different partitions
+                    fitness += self.problema[i][j]
+
+        return round(fitness, 6)

+
+
+

+[docs]
+    def evaluate(self, x: List[int], out: dict, *args, **kwargs) -> None:
+        """
+        Evaluate function for compatibility with pymoo's optimizer.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_09.html b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_09.html new file mode 100644 index 0000000..b8b02aa --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_09.html @@ -0,0 +1,422 @@ + + + + + + pycellga.problems.single_objective.discrete.binary.maxcut20_09 — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.binary.maxcut20_09
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.binary.maxcut20_09

+from problems.abstract_problem import AbstractProblem
+from common import GeneType
+from typing import List
+
+
+

+[docs]
+class Maxcut20_09(AbstractProblem):
+    """
+    Maximum Cut (MAXCUT) function for optimization on a 20-node graph.
+
+    This class evaluates the cut value by summing weights of edges
+    between nodes in different partitions defined by binary variables.
+
+    Attributes
+    ----------
+    problema : list of list of float
+        Adjacency matrix representing edge weights between nodes.
+
+    Methods
+    -------
+    f(x: List[int]) -> float
+        Calculates the MAXCUT function value for a given list of binary variables.
+    evaluate(x: List[int], out: dict) -> None
+        Pymoo-compatible evaluation method for batch processing.
+    """
+
+

+[docs]
+    def __init__(self):
+        """
+        Initialize the MAXCUT problem with binary variables and adjacency matrix.
+        """
+        n_var = 20  # Number of binary variables (nodes)
+        xl = 0  # Lower bound for binary variables
+        xu = 1  # Upper bound for binary variables
+        gen_type = GeneType.BINARY
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)
+
+        # Define adjacency matrix (20x20 matrix of edge weights)
+        self.problema = [
+            [0.000000, 0.130622, 0.694577, 0.922028, 0.335786, 0.359902, 0.279580, 0.880418, 0.201529, 0.313702,
+             0.322765, 0.399944, 0.000000, 0.848267, 0.933051, 0.085267, 0.957646, 0.331033, 0.389269, 0.193177],
+            [0.130622, 0.000000, 0.946416, 0.388165, 0.000000, 0.232571, 0.605770, 0.065642, 0.114155, 0.737786,
+             0.033571, 0.843579, 0.465199, 0.043667, 0.000000, 0.382358, 0.661252, 0.931556, 0.206577, 0.262331],
+            [0.694577, 0.946416, 0.000000, 0.989211, 0.000000, 0.152213, 0.000000, 0.084579, 0.610150, 0.131790,
+             0.950083, 0.426541, 0.721013, 0.428389, 0.308932, 0.861261, 0.479196, 0.863363, 0.000000, 0.110013],
+            [0.922028, 0.388165, 0.989211, 0.000000, 0.785464, 0.227321, 0.469172, 0.032054, 0.574073, 0.736906,
+             0.764415, 0.000000, 0.495863, 0.602718, 0.684042, 0.492622, 0.000000, 0.918634, 0.974679, 0.134843],
+            [0.335786, 0.000000, 0.000000, 0.785464, 0.000000, 0.478171, 0.684823, 0.594988, 0.000000, 0.043655,
+             0.266736, 0.265187, 0.167750, 0.539353, 0.120596, 0.483133, 0.928091, 0.571874, 0.118362, 0.808725],
+            [0.359902, 0.232571, 0.152213, 0.227321, 0.478171, 0.000000, 0.969750, 0.948758, 0.527900, 0.652776,
+             0.990039, 0.945809, 0.831436, 0.355298, 0.049061, 0.103966, 0.897422, 0.732376, 0.491590, 0.526179],
+            [0.279580, 0.605770, 0.000000, 0.469172, 0.684823, 0.969750, 0.000000, 0.652418, 0.123045, 0.368941,
+             0.000000, 0.053590, 0.035474, 0.000000, 0.360846, 0.665888, 0.757456, 0.000000, 0.912162, 0.974535],
+            [0.880418, 0.065642, 0.084579, 0.032054, 0.594988, 0.948758, 0.652418, 0.000000, 0.656499, 0.879623,
+             0.656778, 0.572563, 0.107108, 0.550337, 0.230315, 0.568378, 0.000000, 0.915765, 0.659182, 0.688311],
+            [0.201529, 0.114155, 0.610150, 0.574073, 0.000000, 0.527900, 0.123045, 0.656499, 0.000000, 0.995883,
+             0.172727, 0.442540, 0.974869, 0.000000, 0.997630, 0.035737, 0.835247, 0.139724, 0.859992, 0.000000],
+            [0.313702, 0.737786, 0.131790, 0.736906, 0.043655, 0.652776, 0.368941, 0.879623, 0.995883, 0.000000,
+             0.120131, 0.483339, 0.969497, 0.300482, 0.879444, 0.000000, 0.836946, 0.084211, 0.723167, 0.195939],
+            [0.322765, 0.033571, 0.950083, 0.764415, 0.266736, 0.990039, 0.000000, 0.656778, 0.172727, 0.120131,
+             0.000000, 0.950398, 0.236138, 0.268245, 0.701255, 0.894728, 0.303465, 0.989424, 0.228973, 0.978178],
+            [0.399944, 0.843579, 0.426541, 0.000000, 0.265187, 0.945809, 0.053590, 0.572563, 0.442540, 0.483339,
+             0.950398, 0.000000, 0.060377, 0.854370, 0.488094, 0.581746, 0.935845, 0.723815, 0.225213, 0.424806],
+            [0.000000, 0.465199, 0.721013, 0.495863, 0.167750, 0.831436, 0.035474, 0.107108, 0.974869, 0.969497,
+             0.236138, 0.060377, 0.000000, 0.404249, 0.867185, 0.865152, 0.330739, 0.876005, 0.978220, 0.651577],
+            [0.848267, 0.043667, 0.428389, 0.602718, 0.539353, 0.355298, 0.000000, 0.550337, 0.000000, 0.300482,
+             0.268245, 0.854370, 0.404249, 0.000000, 0.492553, 0.088188, 0.690603, 0.287630, 0.000000, 0.690291],
+            [0.933051, 0.000000, 0.308932, 0.684042, 0.120596, 0.049061, 0.360846, 0.230315, 0.997630, 0.879444,
+             0.701255, 0.488094, 0.867185, 0.492553, 0.000000, 0.000000, 0.593581, 0.076547, 0.297751, 0.159191],
+            [0.085267, 0.382358, 0.861261, 0.492622, 0.483133, 0.103966, 0.665888, 0.568378, 0.035737, 0.000000,
+             0.894728, 0.581746, 0.865152, 0.088188, 0.000000, 0.000000, 0.747596, 0.562290, 0.000000, 0.955731],
+            [0.957646, 0.661252, 0.479196, 0.000000, 0.928091, 0.897422, 0.757456, 0.000000, 0.835247, 0.836946,
+             0.303465, 0.935845, 0.330739, 0.690603, 0.593581, 0.747596, 0.000000, 0.244949, 0.994884, 0.067050],
+            [0.331033, 0.931556, 0.863363, 0.918634, 0.571874, 0.732376, 0.000000, 0.915765, 0.139724, 0.084211,
+             0.989424, 0.723815, 0.876005, 0.287630, 0.076547, 0.562290, 0.244949, 0.000000, 0.621331, 0.752926],
+            [0.389269, 0.206577, 0.000000, 0.974679, 0.118362, 0.491590, 0.912162, 0.659182, 0.859992, 0.723167,
+             0.228973, 0.225213, 0.978220, 0.000000, 0.297751, 0.000000, 0.994884, 0.621331, 0.000000, 0.224879],
+            [0.193177, 0.262331, 0.110013, 0.134843, 0.808725, 0.526179, 0.974535, 0.688311, 0.000000, 0.195939,
+             0.978178, 0.424806, 0.651577, 0.690291, 0.159191, 0.955731, 0.067050, 0.752926, 0.224879, 0.000000]
+        ]

+
+
+

+[docs]
+    def f(self, x: List[int]) -> float:
+        """
+        Calculate the MAXCUT function value for a given list of binary variables.
+
+        Parameters
+        ----------
+        x : List[int]
+            A list of binary variables representing node partitions.
+
+        Returns
+        -------
+        float
+            The MAXCUT function value representing the total weight of edges cut by the partition.
+        """
+        fitness = 0.0
+        cols = len(self.problema)
+
+        for i in range(cols - 1):
+            for j in range(i + 1, cols):
+                if x[i] != x[j]:  # Nodes are in different partitions
+                    fitness += self.problema[i][j]
+
+        return round(fitness, 6)

+
+
+

+[docs]
+    def evaluate(self, x: List[int], out: dict, *args, **kwargs) -> None:
+        """
+        Evaluate function for compatibility with pymoo's optimizer.
+
+        Parameters
+        ----------
+        x : numpy.ndarray
+            Array of input variables.
+        out : dict
+            Dictionary to store the output fitness values.
+        """
+        out["F"] = self.f(x.tolist())

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/binary/mmdp.html b/_modules/pycellga/problems/single_objective/discrete/binary/mmdp.html new file mode 100644 index 0000000..c5e7e60 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/binary/mmdp.html @@ -0,0 +1,384 @@ + + + + + + pycellga.problems.single_objective.discrete.binary.mmdp — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.binary.mmdp
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.binary.mmdp

+from problems.abstract_problem import AbstractProblem
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Mmdp(AbstractProblem):
+    """
+    Represents the Massively Multimodal Deceptive Problem (MMDP).
+
+    The MMDP is designed to deceive genetic algorithms by having multiple local 
+    optima. The problem is characterized by a chromosome length of 240 and a 
+    maximum fitness value of 40.
+
+    Attributes
+    ----------
+    gen_type : GeneType
+        Type of genes used in the problem (binary in this case).
+    n_var : int
+        The number of design variables (240 for MMDP).
+    xl : float
+        The lower bound  for the design variables (0 for binary genes).
+    xu : float
+        The upper bound for the design variables (1 for binary genes).
+
+    Methods
+    -------
+    f(x: list) -> float
+        Evaluates the fitness of a given chromosome.
+    """
+
+

+[docs]
+    def __init__(self):
+        """
+        Initializes the MMDP problem with binary genes, 240 design variables, 
+        and predefined bounds.
+        """
+        n_var = 240  
+        xl = 0
+        xu = 1
+        gen_type=GeneType.BINARY
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[int]) -> float:
+        """
+        Evaluates the fitness of a given chromosome for the MMDP.
+
+        The fitness function is calculated based on the number of ones in each 
+        of the 40 subproblems, each of length 6.
+
+        Parameters
+        ----------
+        x : List[int]
+            A list representing the chromosome, where each element is a binary 
+            value (0 or 1).
+
+        Returns
+        -------
+        float
+            The normalized fitness value of the chromosome, rounded to three 
+            decimal places.
+        """
+        subproblems_length = 6
+        subproblems_number = 40
+        fitness = 0.0
+
+        for i in range(subproblems_number):
+            total_ones = sum(x[i * subproblems_length + j] for j in range(subproblems_length))
+
+            if total_ones == 0 or total_ones == 6:
+                partial_fitness = 1.0
+            elif total_ones == 1 or total_ones == 5:
+                partial_fitness = 0.0
+            elif total_ones == 2 or total_ones == 4:
+                partial_fitness = 0.360384
+            elif total_ones == 3:
+                partial_fitness = 0.640576
+
+            fitness += partial_fitness
+
+        fitness_normalized = fitness / subproblems_number
+        return round(fitness_normalized, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/binary/one_max.html b/_modules/pycellga/problems/single_objective/discrete/binary/one_max.html new file mode 100644 index 0000000..f52631f --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/binary/one_max.html @@ -0,0 +1,366 @@ + + + + + + pycellga.problems.single_objective.discrete.binary.one_max — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.binary.one_max
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.binary.one_max

+from problems.abstract_problem import AbstractProblem
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class OneMax(AbstractProblem):
+    """
+    Represents the OneMax problem.
+
+    The OneMax problem is a simple genetic algorithm benchmark problem 
+    where the fitness of a chromosome is the sum of its bits.
+
+    Attributes
+    ----------
+    gen_type : GeneType
+        Type of genes used in the problem (binary in this case).
+    n_var : int
+        The number of design variables (default is 100).
+    xl : float
+        The lower bound for the design variables (0 for binary genes).
+    xu : float
+        The upper bound for the design variables (1 for binary genes).
+
+    Methods
+    -------
+    f(x: list) -> float
+        Evaluates the fitness of a given chromosome.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 100):
+        """
+        Initialize the OneMax problem with a default number of variables (100) 
+        and binary gene bounds.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of design variables (default is 100).
+        """
+        xl = 0
+        xu = 1
+        gen_type=GeneType.BINARY
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)

+
+
+

+[docs]
+    def f(self, x: List[int]) -> float:
+        """
+        Evaluates the fitness of a given chromosome for the OneMax problem.
+
+        The fitness function is the sum of all bits in the chromosome.
+
+        Parameters
+        ----------
+        x : List[int]
+            A list representing the chromosome, where each element is a binary 
+            value (0 or 1).
+
+        Returns
+        -------
+        float
+            The fitness value of the chromosome, which is the sum of its bits.
+        """
+        return float(-sum(x))

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/binary/peak.html b/_modules/pycellga/problems/single_objective/discrete/binary/peak.html new file mode 100644 index 0000000..6c1083b --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/binary/peak.html @@ -0,0 +1,383 @@ + + + + + + pycellga.problems.single_objective.discrete.binary.peak — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.binary.peak
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.binary.peak

+from problems.abstract_problem import AbstractProblem
+from numpy import random
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Peak(AbstractProblem):
+    """
+    Represents the Peak problem.
+
+    The Peak problem evaluates the fitness of a chromosome based on its 
+    distance to a set of target peaks.
+
+    Attributes
+    ----------
+    gen_type : GeneType
+        Type of genes used in the problem (binary in this case).
+    n_var : int
+        The number of design variables (chromosome length, default is 100).
+    xl : float
+        The lower bounds for the design variables (0 for binary genes).
+    xu : float
+        The upper bounds for the design variables (1 for binary genes).
+
+    Methods
+    -------
+    f(x: list) -> float
+        Evaluates the fitness of a given chromosome.
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 100):
+        """
+        Initialize the Peak problem with a default number of variables (100) 
+        and binary gene bounds.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of design variables (default is 100).
+        """
+        xl = 0
+        xu = 1
+        gen_type=GeneType.BINARY
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)
+
+        # Seed the random number generator for reproducibility
+        random.seed(100)
+        self.p_target = [
+            [random.randint(2) for _ in range(n_var)] for _ in range(100)
+        ]  # 100 target peaks

+
+
+

+[docs]
+    def f(self, x: List[int]) -> float:
+        """
+        Evaluates the fitness of a given chromosome for the Peak problem.
+
+        The fitness function calculates the distance between the given 
+        chromosome and a set of randomly generated target peaks.
+
+        Parameters
+        ----------
+        x : list
+            A list representing the chromosome, where each element is a binary 
+            value (0 or 1).
+
+        Returns
+        -------
+        float
+            The fitness value of the chromosome, normalized to a range of 0.0 to 1.0.
+        """
+        problem_length = len(x)
+        min_distance = float("inf")
+
+        for peak in self.p_target:
+            distance = sum(1 for xi, pi in zip(x, peak) if xi != pi)
+            if distance < min_distance:
+                min_distance = distance
+
+        # Normalize the fitness value
+        fitness = 1 - (min_distance / problem_length)  # 1 - normalized distance
+        return round(fitness, 3)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/problems/single_objective/discrete/permutation/tsp.html b/_modules/pycellga/problems/single_objective/discrete/permutation/tsp.html new file mode 100644 index 0000000..c478fa8 --- /dev/null +++ b/_modules/pycellga/problems/single_objective/discrete/permutation/tsp.html @@ -0,0 +1,412 @@ + + + + + + pycellga.problems.single_objective.discrete.permutation.tsp — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.problems.single_objective.discrete.permutation.tsp
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.problems.single_objective.discrete.permutation.tsp

+from problems.abstract_problem import AbstractProblem
+import tsplib95
+from math import sqrt
+from geopy.distance import geodesic
+import os
+from typing import List
+from common import GeneType
+
+
+

+[docs]
+class Tsp(AbstractProblem):
+    """
+    Represents the Traveling Salesman Problem (TSP).
+
+    This class solves the TSP using geographical distances (GEO) for node coordinates.
+
+    Attributes
+    ----------
+    gen_type : GeneType
+        The type of genes used in the problem (permutation in this case).
+    n_var : int
+        The number of nodes in the TSP problem.
+    xl : int
+        The minimum value for each variable (1 in this case, node index starts at 1).
+    xu : int
+        The maximum value for each variable (number of nodes).
+    """
+
+

+[docs]
+    def __init__(self, n_var: int = 14):
+        """
+        Initialize the TSP problem with default attributes.
+
+        Parameters
+        ----------
+        n_var : int, optional
+            Number of nodes in the TSP problem (default is 14).
+        """
+        xl = [1] 
+        xu = [14]
+        gen_type=GeneType.PERMUTATION
+
+        super().__init__(gen_type=gen_type, n_var=n_var, xl=xl, xu=xu)
+
+        # Load TSP data file
+        file_path = os.path.join(os.path.dirname(__file__), "burma14.tsp.txt")
+        with open(file_path) as fl:
+            self.problem = tsplib95.read(fl)
+            self.node_coords = list(self.problem.node_coords.values())
+
+        # Precompute distances
+        self.distances = self._compute_distances()

+
+
+    def _compute_distances(self):
+        """
+        Precomputes the geographical distances between all node pairs.
+
+        Returns
+        -------
+        dict
+            A dictionary with distances between all node pairs.
+        """
+        distances = {}
+        for i, coord_a in enumerate(self.node_coords):
+            distances[i + 1] = {}
+            for j, coord_b in enumerate(self.node_coords):
+                distances[i + 1][j + 1] = self.geographical_dist(coord_a, coord_b)
+        return distances
+
+

+[docs]
+    def f(self, x: List[int]) -> float:
+        """
+        Evaluates the fitness of a given chromosome (route) for the TSP.
+
+        Parameters
+        ----------
+        x : list
+            A list representing the route (chromosome), where each element is a node index.
+
+        Returns
+        -------
+        float
+            The total distance of the route, rounded to one decimal place.
+        """
+        fitness = 0.0
+        for i in range(len(x)):
+            start_node = x[i]
+            end_node = x[(i + 1) % len(x)]
+            fitness += self.distances[start_node][end_node]
+        return round(fitness, 1)

+
+
+

+[docs]
+    def geographical_dist(self, a: List[float], b: List[float]) -> float:
+        """
+        Computes the geographical distance between two nodes using the geodesic distance.
+
+        Parameters
+        ----------
+        a : list
+            Coordinates of the first node.
+        b : list
+            Coordinates of the second node.
+
+        Returns
+        -------
+        float
+            The geographical distance between the two nodes, rounded to one decimal place.
+        """
+        dist = geodesic(a, b).km
+        return round(dist, 1)

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/arithmetic_crossover.html b/_modules/pycellga/recombination/arithmetic_crossover.html new file mode 100644 index 0000000..ea90443 --- /dev/null +++ b/_modules/pycellga/recombination/arithmetic_crossover.html @@ -0,0 +1,379 @@ + + + + + + pycellga.recombination.arithmetic_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.arithmetic_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.arithmetic_crossover

+import random
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class ArithmeticCrossover(RecombinationOperator):
+    """
+    ArithmeticCrossover performs an arithmetic crossover operation on a pair of parent individuals
+    to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the ArithmeticCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the arithmetic crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        offsprings = []
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+        chsize = len(p1.chromosome)
+        a = p1.chromosome
+        b = p2.chromosome
+
+        alpha = random.uniform(0, 1)  # Random weight factor
+        child_1_ch = [0 for i in range(chsize)]
+        child_2_ch = [0 for i in range(chsize)]
+
+        for i in range(chsize):
+            child_1_ch[i] = round(alpha * a[i] + (1 - alpha) * b[i], 5)
+            child_2_ch[i] = round((1 - alpha) * a[i] + alpha * b[i], 5)
+
+        # Create the first child
+        child_1 = Individual()
+        child_1.chromosome = child_1_ch
+        child_1.ch_size = len(child_1_ch)
+        child_1.position = p1.position
+        child_1.neighbors_positions = p1.neighbors_positions
+        child_1.fitness_value = self.problem.f(child_1_ch)
+        offsprings.append(child_1)
+
+        # Create the second child
+        child_2 = Individual()
+        child_2.chromosome = child_2_ch
+        child_2.ch_size = len(child_2_ch)
+        child_2.position = p2.position
+        child_2.neighbors_positions = p2.neighbors_positions
+        child_2.fitness_value = self.problem.f(child_2_ch)
+        offsprings.append(child_2)
+
+        return offsprings

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/blxalpha_crossover.html b/_modules/pycellga/recombination/blxalpha_crossover.html new file mode 100644 index 0000000..acf2cd8 --- /dev/null +++ b/_modules/pycellga/recombination/blxalpha_crossover.html @@ -0,0 +1,408 @@ + + + + + + pycellga.recombination.blxalpha_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.blxalpha_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.blxalpha_crossover

+import random
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class BlxalphaCrossover(RecombinationOperator):
+    """
+    BlxalphaCrossover performs BLX-alpha crossover on a pair of parent individuals
+    to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the BlxalphaCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
+        """
+        Combine two parent individuals using BLX-alpha crossover to produce a single offspring.
+
+        Parameters
+        ----------
+        p1 : Individual
+            The first parent individual.
+        p2 : Individual
+            The second parent individual.
+        locationsource : Individual
+            The individual from which to copy positional information for the offspring.
+
+        Returns
+        -------
+        Individual
+            The resulting offspring individual.
+        """
+        chsize = len(p1.chromosome)
+        child = [0 for i in range(chsize)]
+        alpha = random.uniform(0, 1)
+        c_min = 0.0
+        c_max = 0.0
+        min_range = 0.0
+        max_range = 0.0
+
+        for i in range(chsize):
+            p1_allele = p1.chromosome[i]
+            p2_allele = p2.chromosome[i]
+
+            if p1_allele > p2_allele:
+                c_max = p1_allele
+                c_min = p2_allele
+            elif p1_allele < p2_allele:
+                c_max = p2_allele
+                c_min = p1_allele
+            else:
+                c_max = p1_allele  # or p2_allele because it is the case where p1_allele=p2_allele
+
+            l = c_max - c_min
+            min_range = c_min - (l * alpha)
+            max_range = c_max + (l * alpha)
+
+            new_allele = random.uniform(min_range, max_range)
+            child[i] = round(new_allele, 5)
+
+        indv = Individual(p1.gen_type, p1.ch_size)
+        indv.position = locationsource.position
+        indv.neighbors_positions = locationsource.neighbors_positions
+        indv.chromosome = list(child)
+        indv.fitness_value = self.problem.f(child)
+        return indv

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the BLX-alpha crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+
+        return [
+            self.combine(p1, p2, p1),
+            self.combine(p1, p2, p2)
+        ]

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/byte_one_point_crossover.html b/_modules/pycellga/recombination/byte_one_point_crossover.html new file mode 100644 index 0000000..0fe483e --- /dev/null +++ b/_modules/pycellga/recombination/byte_one_point_crossover.html @@ -0,0 +1,398 @@ + + + + + + pycellga.recombination.byte_one_point_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.byte_one_point_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.byte_one_point_crossover

+import numpy as np
+from individual import Individual
+from problems.abstract_problem import AbstractProblem
+from typing import List
+import struct
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class ByteOnePointCrossover(RecombinationOperator):
+    """
+    ByteOnePointCrossover operator defined in (Satman, 2013). ByteOnePointCrossover performs a 
+    one-point crossover at the byte level on a pair of parent individuals to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the ByteOnePointCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the one-point crossover on the parent individuals at the byte level to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        offsprings = []
+        child_ch1 = []
+        child_ch2 = []
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+
+        for k in range(len(p1.chromosome)):
+            # Convert the k-th gene of the parents to bytes
+            p1_byte_ch = list(struct.pack("d", p1.chromosome[k]))
+            p2_byte_ch = list(struct.pack("d", p2.chromosome[k]))
+
+            # Determine crossover point
+            co_point = np.random.randint(1, len(p1_byte_ch))
+
+            # Segment the bytes at the crossover point
+            p1_seg1 = p1_byte_ch[0:co_point]
+            p1_seg2 = p1_byte_ch[co_point:]
+
+            p2_seg1 = p2_byte_ch[0:co_point]
+            p2_seg2 = p2_byte_ch[co_point:]
+
+            # First child
+            new_chromosome_1_part = p1_seg1 + p2_seg2
+            child_part_byte_1 = bytearray(new_chromosome_1_part)
+            child_part_float_1 = struct.unpack("d", child_part_byte_1)[0]
+            child_ch1.append(round(child_part_float_1, 5))
+
+            # Second child
+            new_chromosome_2_part = p1_seg2 + p2_seg1
+            child_part_byte_2 = bytearray(new_chromosome_2_part)
+            child_part_float_2 = struct.unpack("d", child_part_byte_2)[0]
+            child_ch2.append(round(child_part_float_2, 5))
+
+        # Create the first child individual
+        child_1 = Individual()
+        child_1.position = p1.position
+        child_1.neighbors_positions = p1.neighbors_positions
+        child_1.fitness_value = self.problem.f(child_ch1)
+        child_1.chromosome = child_ch1
+        child_1.ch_size = len(child_1.chromosome)
+        offsprings.append(child_1)
+
+        # Create the second child individual
+        child_2 = Individual()
+        child_2.position = p2.position
+        child_2.neighbors_positions = p2.neighbors_positions
+        child_2.fitness_value = self.problem.f(child_ch2)
+        child_2.chromosome = child_ch2
+        child_2.ch_size = len(child_2.chromosome)
+        offsprings.append(child_2)
+
+        return offsprings

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/byte_uniform_crossover.html b/_modules/pycellga/recombination/byte_uniform_crossover.html new file mode 100644 index 0000000..ff22a6a --- /dev/null +++ b/_modules/pycellga/recombination/byte_uniform_crossover.html @@ -0,0 +1,400 @@ + + + + + + pycellga.recombination.byte_uniform_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.byte_uniform_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.byte_uniform_crossover

+import numpy.random as randomgenerator
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from typing import List
+import struct
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class ByteUniformCrossover(RecombinationOperator):
+    """
+    ByteUniformCrossover operator defined in (Satman, 2013). ByteUniformCrossover performs a 
+    uniform crossover at the byte level on a pair of parent individuals to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the ByteUniformCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
+        """
+        Combine two parent individuals using uniform crossover at the byte level to produce a single offspring.
+
+        Parameters
+        ----------
+        p1 : Individual
+            The first parent individual.
+        p2 : Individual
+            The second parent individual.
+        locationsource : Individual
+            The individual from which to copy positional information for the offspring.
+
+        Returns
+        -------
+        Individual
+            The resulting offspring individual.
+        """
+        child_ch = []
+        for k in range(len(p1.chromosome)):
+            # Convert the k-th gene of the parents to bytes
+            p1_byte_ch = list(struct.pack("d", p1.chromosome[k]))
+            p2_byte_ch = list(struct.pack("d", p2.chromosome[k]))
+
+            chsize = len(p1_byte_ch)
+            child_part = [0 for i in range(len(p1_byte_ch))]
+            for i in range(chsize):
+                if randomgenerator.rand() < 0.5:
+                    child_part[i] = p1_byte_ch[i]
+                else:
+                    child_part[i] = p2_byte_ch[i]
+
+            child_part_byte = bytearray(child_part)
+            child_part_float = list(struct.unpack("d", child_part_byte))
+
+            child_ch.append(round(child_part_float[0], 5))
+
+        indv = Individual(p1.gen_type, p1.ch_size)
+        indv.position = locationsource.position
+        indv.neighbors_positions = locationsource.neighbors_positions
+        indv.chromosome = list(child_ch)
+        indv.fitness_value = self.problem.f(child_ch)
+        return indv

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the uniform crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+
+        return [
+            self.combine(p1, p2, p1),
+            self.combine(p1, p2, p2)
+        ]

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/flat_crossover.html b/_modules/pycellga/recombination/flat_crossover.html new file mode 100644 index 0000000..5dc3f8c --- /dev/null +++ b/_modules/pycellga/recombination/flat_crossover.html @@ -0,0 +1,396 @@ + + + + + + pycellga.recombination.flat_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.flat_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.flat_crossover

+import random
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class FlatCrossover(RecombinationOperator):
+    """
+    FlatCrossover performs a flat crossover on a pair of parent individuals
+    to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the FlatCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
+        """
+        Combine two parent individuals using flat crossover to produce a single offspring.
+
+        Parameters
+        ----------
+        p1 : Individual
+            The first parent individual.
+        p2 : Individual
+            The second parent individual.
+        locationsource : Individual
+            The individual from which to copy positional information for the offspring.
+
+        Returns
+        -------
+        Individual
+            The resulting offspring individual.
+        """
+        chsize = len(p1.chromosome)
+        child = [0 for i in range(chsize)]
+        for i in range(chsize):
+            p1_allele = p1.chromosome[i]
+            p2_allele = p2.chromosome[i]
+
+            if p1_allele > p2_allele:
+                c_max = p1_allele
+                c_min = p2_allele
+            else:
+                c_max = p2_allele
+                c_min = p1_allele
+
+            new_allele = random.uniform(c_min, c_max)
+            child[i] = round(new_allele, 5)
+
+        indv = Individual(p1.gen_type, p1.ch_size)
+        indv.position = locationsource.position
+        indv.neighbors_positions = locationsource.neighbors_positions
+        indv.chromosome = list(child)
+        indv.fitness_value = self.problem.f(child)
+        return indv

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the flat crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+
+        return [
+            self.combine(p1, p2, p1),
+            self.combine(p1, p2, p2)
+        ]

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/linear_crossover.html b/_modules/pycellga/recombination/linear_crossover.html new file mode 100644 index 0000000..9017840 --- /dev/null +++ b/_modules/pycellga/recombination/linear_crossover.html @@ -0,0 +1,401 @@ + + + + + + pycellga.recombination.linear_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.linear_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.linear_crossover

+import random
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class LinearCrossover(RecombinationOperator):
+    """
+    LinearCrossover performs a linear crossover on a pair of parent individuals
+    to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the LinearCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
+        """
+        Combine two parent individuals using linear crossover to produce a single offspring.
+
+        Parameters
+        ----------
+        p1 : Individual
+            The first parent individual.
+        p2 : Individual
+            The second parent individual.
+        locationsource : Individual
+            The individual from which to copy positional information for the offspring.
+
+        Returns
+        -------
+        Individual
+            The resulting offspring individual.
+        """
+        chsize = len(p1.chromosome)
+        child1_ch = [0 for i in range(chsize)]
+        child2_ch = [0 for i in range(chsize)]
+        child3_ch = [0 for i in range(chsize)]
+
+        for i in range(chsize):
+            p1_allele = p1.chromosome[i]
+            p2_allele = p2.chromosome[i]
+
+            child1_ch[i] = round(0.5 * p1_allele + 0.5 * p2_allele, 5)
+            child2_ch[i] = round(3 * p1_allele / 2 - 0.5 * p2_allele, 5)
+            child3_ch[i] = round(-0.5 * p1_allele + 3 * p2_allele / 2, 5)
+
+        chosed_ch = random.randint(1, 3)
+        if chosed_ch == 1:
+            chosed_child_ch = child1_ch
+        elif chosed_ch == 2:
+            chosed_child_ch = child2_ch
+        elif chosed_ch == 3:
+            chosed_child_ch = child3_ch
+
+        indv = Individual(p1.gen_type, p1.ch_size)
+        indv.position = locationsource.position
+        indv.neighbors_positions = locationsource.neighbors_positions
+        indv.chromosome = list(chosed_child_ch)
+        indv.fitness_value = self.problem.f(chosed_child_ch)
+        return indv

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the linear crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+
+        return [
+            self.combine(p1, p2, p1),
+            self.combine(p1, p2, p2)
+        ]

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/one_point_crossover.html b/_modules/pycellga/recombination/one_point_crossover.html new file mode 100644 index 0000000..6464410 --- /dev/null +++ b/_modules/pycellga/recombination/one_point_crossover.html @@ -0,0 +1,380 @@ + + + + + + pycellga.recombination.one_point_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.one_point_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.one_point_crossover

+import numpy as np
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from recombination.recombination_operator import RecombinationOperator
+
+
+

+[docs]
+class OnePointCrossover(RecombinationOperator):
+    """
+    OnePointCrossover performs a one-point crossover on a pair of parent individuals
+    to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the OnePointCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the one-point crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        offsprings = []
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+
+        # Determine crossover point
+        co_point = np.random.randint(len(p1.chromosome))
+
+        # Segment the chromosomes at the crossover point
+        p1_seg1 = list(p1.chromosome[0:co_point])
+        p1_seg2 = list(p1.chromosome[co_point:])
+        p2_seg1 = list(p2.chromosome[0:co_point])
+        p2_seg2 = list(p2.chromosome[co_point:])
+
+        # Create the first child
+        child_1 = Individual()
+        new_chromosome_1 = p1_seg1 + p2_seg2
+        child_1.chromosome = new_chromosome_1
+        child_1.ch_size = len(new_chromosome_1)
+        child_1.position = p1.position
+        child_1.neighbors_positions = p1.neighbors_positions
+        child_1.fitness_value = self.problem.f(child_1.chromosome)
+        offsprings.append(child_1)
+
+        # Create the second child
+        child_2 = Individual()
+        new_chromosome_2 = p1_seg2 + p2_seg1
+        child_2.chromosome = new_chromosome_2
+        child_2.ch_size = len(new_chromosome_2)
+        child_2.position = p2.position
+        child_2.neighbors_positions = p2.neighbors_positions
+        child_2.fitness_value = self.problem.f(child_2.chromosome)
+        offsprings.append(child_2)
+
+        return offsprings

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/pmx_crossover.html b/_modules/pycellga/recombination/pmx_crossover.html new file mode 100644 index 0000000..aa287f7 --- /dev/null +++ b/_modules/pycellga/recombination/pmx_crossover.html @@ -0,0 +1,432 @@ + + + + + + pycellga.recombination.pmx_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.pmx_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.pmx_crossover

+from individual import *
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class PMXCrossover(RecombinationOperator):
+    """
+    PMXCrossover performs Partially Mapped Crossover (PMX) on a pair of parent individuals
+    to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the PMXCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the PMX crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        offsprings = []
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+
+        co_point_1 = 15
+        co_point_2 = 35
+
+        p1_seg_main = list(p1.chromosome[co_point_1:co_point_2])
+        p1_seg_left = list(p1.chromosome[0:co_point_1])
+        p1_seg_right = list(p1.chromosome[co_point_2:])
+        p1_seg_left_aux = list()
+        p1_seg_right_aux = list()
+
+        p2_seg_main = list(p2.chromosome[co_point_1:co_point_2])
+        p2_seg_left = list(p2.chromosome[0:co_point_1])
+        p2_seg_right = list(p2.chromosome[co_point_2:])
+        p2_seg_left_aux = list()
+        p2_seg_right_aux = list()
+
+        # First child
+        child_1 = Individual()
+
+        for seg in p1_seg_left:
+            if seg in p2_seg_main:
+                mapping_index = p2_seg_main.index(seg)
+                mapping_item = p1_seg_main[mapping_index]
+                while mapping_item in p2_seg_main:
+                    mapping_index = p2_seg_main.index(mapping_item)
+                    mapping_item = p1_seg_main[mapping_index]
+                p1_seg_left_aux.append(mapping_item)
+            else:
+                p1_seg_left_aux.append(seg)
+
+        for seg in p1_seg_right:
+            if seg in p2_seg_main:
+                mapping_index = p2_seg_main.index(seg)
+                mapping_item = p1_seg_main[mapping_index]
+                while mapping_item in p2_seg_main:
+                    mapping_index = p2_seg_main.index(mapping_item)
+                    mapping_item = p1_seg_main[mapping_index]
+                p1_seg_right_aux.append(mapping_item)
+            else:
+                p1_seg_right_aux.append(seg)
+
+        child_1_ch = p1_seg_left_aux + p2_seg_main + p1_seg_right_aux
+
+        child_1.chromosome = child_1_ch
+        child_1.ch_size = len(child_1_ch)
+        child_1.position = p1.position
+        child_1.neighbors_positions = p1.neighbors_positions
+        child_1.fitness_value = self.problem.f(child_1.chromosome)
+        offsprings.append(child_1)
+
+        # Second child
+        child_2 = Individual()
+
+        for seg in p2_seg_left:
+            if seg in p1_seg_main:
+                mapping_index = p1_seg_main.index(seg)
+                mapping_item = p2_seg_main[mapping_index]
+                while mapping_item in p1_seg_main:
+                    mapping_index = p1_seg_main.index(mapping_item)
+                    mapping_item = p2_seg_main[mapping_index]
+                p2_seg_left_aux.append(mapping_item)
+            else:
+                p2_seg_left_aux.append(seg)
+
+        for seg in p2_seg_right:
+            if seg in p1_seg_main:
+                mapping_index = p1_seg_main.index(seg)
+                mapping_item = p2_seg_main[mapping_index]
+                while mapping_item in p1_seg_main:
+                    mapping_index = p1_seg_main.index(mapping_item)
+                    mapping_item = p2_seg_main[mapping_index]
+                p2_seg_right_aux.append(mapping_item)
+            else:
+                p2_seg_right_aux.append(seg)
+
+        child_2_ch = p2_seg_left_aux + p1_seg_main + p2_seg_right_aux
+
+        child_2.chromosome = child_2_ch
+        child_2.ch_size = len(child_2_ch)
+        child_2.position = p2.position
+        child_2.neighbors_positions = p2.neighbors_positions
+        child_2.fitness_value = self.problem.f(child_2.chromosome)
+        offsprings.append(child_2)
+
+        return offsprings

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/two_point_crossover.html b/_modules/pycellga/recombination/two_point_crossover.html new file mode 100644 index 0000000..1f435d1 --- /dev/null +++ b/_modules/pycellga/recombination/two_point_crossover.html @@ -0,0 +1,387 @@ + + + + + + pycellga.recombination.two_point_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.two_point_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.two_point_crossover

+import numpy as np
+from individual import Individual
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class TwoPointCrossover(RecombinationOperator):
+    """
+    TwoPointCrossover performs a two-point crossover on a pair of parent individuals
+    to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the TwoPointCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the two-point crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        offsprings = []
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+
+        parameter = len(p1.chromosome) * 0.20
+        min_co = int(parameter)
+        max_co = len(p1.chromosome) - (int(parameter) - 1)
+
+        co_point_1 = np.random.randint(min_co, max_co)
+        co_point_2 = np.random.randint(min_co, max_co)
+
+        if co_point_1 > co_point_2:
+            co_point_1, co_point_2 = co_point_2, co_point_1
+
+        P1_seg_1 = p1.chromosome[0:co_point_1]
+        P1_seg_2 = p1.chromosome[co_point_1:co_point_2]
+        P1_seg_3 = p1.chromosome[co_point_2:]
+
+        P2_seg_1 = p2.chromosome[0:co_point_1]
+        P2_seg_2 = p2.chromosome[co_point_1:co_point_2]
+        P2_seg_3 = p2.chromosome[co_point_2:]
+
+        child_1_chromosome = P1_seg_1 + P2_seg_2 + P1_seg_3
+        child_2_chromosome = P2_seg_1 + P1_seg_2 + P2_seg_3
+
+        child_1 = Individual()
+        child_1.chromosome = child_1_chromosome
+        child_1.ch_size = len(child_1_chromosome)
+        child_1.position = p1.position
+        child_1.neighbors_positions = p1.neighbors_positions
+        child_1.fitness_value = self.problem.f(child_1.chromosome)
+        offsprings.append(child_1)
+
+        child_2 = Individual()
+        child_2.chromosome = child_2_chromosome
+        child_2.ch_size = len(child_2_chromosome)
+        child_2.position = p2.position
+        child_2.neighbors_positions = p2.neighbors_positions
+        child_2.fitness_value = self.problem.f(child_2.chromosome)
+        offsprings.append(child_2)
+
+        return offsprings

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/unfair_avarage_crossover.html b/_modules/pycellga/recombination/unfair_avarage_crossover.html new file mode 100644 index 0000000..700ffc4 --- /dev/null +++ b/_modules/pycellga/recombination/unfair_avarage_crossover.html @@ -0,0 +1,386 @@ + + + + + + pycellga.recombination.unfair_avarage_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.unfair_avarage_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.unfair_avarage_crossover

+import random
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class UnfairAvarageCrossover(RecombinationOperator):
+    """
+    UnfairAvarageCrossover performs an unfair average crossover on a pair of parent individuals
+    to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the UnfairAvarageCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the unfair average crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        offsprings = []
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+        chsize = len(p1.chromosome)
+        a = p1.chromosome
+        b = p2.chromosome
+
+        alpha = random.uniform(0, 1)
+        child_1_ch = [0 for i in range(chsize)]
+        child_2_ch = [0 for i in range(chsize)]
+
+        alpha = random.uniform(0, 1)
+        j = random.randint(1, chsize)
+
+        for i in range(chsize):
+            if i <= j:
+                child_1_ch[i] = round((1 + alpha) * a[i] - alpha * b[i], 5)
+                child_2_ch[i] = round((1 - alpha) * a[i] + alpha * b[i], 5)
+            else:
+                child_1_ch[i] = round(-alpha * a[i] + (1 + alpha) * b[i], 5)
+                child_2_ch[i] = round(alpha * a[i] + (1 - alpha) * b[i], 5)
+
+        # First child
+        child_1 = Individual()
+        child_1.chromosome = child_1_ch
+        child_1.ch_size = len(child_1_ch)
+        child_1.position = p1.position
+        child_1.neighbors_positions = p1.neighbors_positions
+        child_1.fitness_value = self.problem.f(child_1_ch)
+        offsprings.append(child_1)
+
+        # Second child
+        child_2 = Individual()
+        child_2.chromosome = child_2_ch
+        child_2.ch_size = len(child_2_ch)
+        child_2.position = p2.position
+        child_2.neighbors_positions = p2.neighbors_positions
+        child_2.fitness_value = self.problem.f(child_2_ch)
+        offsprings.append(child_2)
+
+        return offsprings

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/recombination/uniform_crossover.html b/_modules/pycellga/recombination/uniform_crossover.html new file mode 100644 index 0000000..719f867 --- /dev/null +++ b/_modules/pycellga/recombination/uniform_crossover.html @@ -0,0 +1,388 @@ + + + + + + pycellga.recombination.uniform_crossover — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.recombination.uniform_crossover
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.recombination.uniform_crossover

+import numpy.random as randomgenerator
+from individual import *
+from problems.abstract_problem import AbstractProblem
+from typing import List
+from recombination.recombination_operator import RecombinationOperator
+
+

+[docs]
+class UniformCrossover(RecombinationOperator):
+    """
+    UniformCrossover performs a uniform crossover on a pair of parent individuals
+    to produce offspring individuals.
+
+    Parameters
+    ----------
+    parents : list
+        A list containing two parent individuals.
+    problem : AbstractProblem
+        The problem instance that provides the fitness function.
+    """
+
+

+[docs]
+    def __init__(self, parents: list, problem: AbstractProblem):
+        """
+        Initialize the UniformCrossover object.
+
+        Parameters
+        ----------
+        parents : list
+            A list containing two parent individuals.
+        problem : AbstractProblem
+            The problem instance that provides the fitness function.
+        """
+        self.parents = parents
+        self.problem = problem

+
+
+

+[docs]
+    def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
+        """
+        Combine two parent individuals using uniform crossover to produce a single offspring.
+
+        Parameters
+        ----------
+        p1 : Individual
+            The first parent individual.
+        p2 : Individual
+            The second parent individual.
+        locationsource : Individual
+            The individual from which to copy positional information for the offspring.
+
+        Returns
+        -------
+        Individual
+            The resulting offspring individual.
+        """
+        chsize = len(p1.chromosome)
+        child = [0 for i in range(chsize)]
+        for i in range(chsize):
+            if randomgenerator.rand() < 0.5:
+                child[i] = p1.chromosome[i]
+            else:
+                child[i] = p2.chromosome[i]
+
+        indv = Individual(p1.gen_type, p1.ch_size)
+        indv.position = locationsource.position
+        indv.neighbors_positions = locationsource.neighbors_positions
+        indv.chromosome = list(child)
+        indv.fitness_value = self.problem.f(child)
+        return indv

+
+
+

+[docs]
+    def get_recombinations(self) -> List[Individual]:
+        """
+        Perform the uniform crossover on the parent individuals to produce offspring.
+
+        Returns
+        -------
+        List[Individual]
+            A list containing the offspring individuals.
+        """
+        p1 = self.parents[0]
+        p2 = self.parents[1]
+
+        return [
+            self.combine(p1, p2, p1),
+            self.combine(p1, p2, p2)
+        ]

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/selection/roulette_wheel_selection.html b/_modules/pycellga/selection/roulette_wheel_selection.html new file mode 100644 index 0000000..e8b30db --- /dev/null +++ b/_modules/pycellga/selection/roulette_wheel_selection.html @@ -0,0 +1,375 @@ + + + + + + pycellga.selection.roulette_wheel_selection — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.selection.roulette_wheel_selection
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.selection.roulette_wheel_selection

+from typing import List
+from individual import Individual
+from selection.selection_operator import SelectionOperator
+import random
+
+

+[docs]
+class RouletteWheelSelection(SelectionOperator):
+    """
+    RouletteWheelSelection performs a roulette wheel selection on a population of individuals
+    to select parent individuals for crossover.
+
+    Parameters
+    ----------
+    pop_list : list of Individual
+        The population of individuals to select from.
+    c : int
+        The index of the individual to start selection from.
+    """
+
+

+[docs]
+    def __init__(self, pop_list: List[Individual] = [], c: int = 0):
+        """
+        Initialize the RouletteWheelSelection object.
+
+        Parameters
+        ----------
+        pop_list : list of Individual
+            The population of individuals to select from.
+        c : int
+            The index of the individual to start selection from.
+        """
+        self.pop_list = pop_list
+        self.c = c

+
+
+

+[docs]
+    def get_parents(self) -> List[Individual]:
+        """
+        Perform the roulette wheel selection to get parent individuals.
+
+        Returns
+        -------
+        list of Individual
+            A list containing the selected parent individuals.
+        """
+        parents = []
+        p1 = self.pop_list[self.c - 1]
+
+        parents.append(p1)
+        neighbors_positions = p1.neighbors_positions
+        neighbors = []
+
+        # Find neighbors in the population
+        for i in range(len(self.pop_list)):
+            if self.pop_list[i].position in neighbors_positions:
+                neighbors.append(self.pop_list[i])
+
+        # Calculate the sum of neighbors' fitness values
+        neighbors_fitness_sum = 0
+        neighbors_fitnesses = []
+        for neighbor in neighbors:
+            neighbors_fitness_sum += neighbor.fitness_value
+            neighbors_fitnesses.append(neighbor.fitness_value)
+
+        # Perform roulette wheel selection
+        random_number = random.uniform(0, 1)
+        previous_probability = 0
+        for neighbor in neighbors:
+            previous_probability += (neighbor.fitness_value / neighbors_fitness_sum)
+
+            if p1 != neighbor:
+                if random_number < previous_probability:
+                    parents.append(neighbor)
+                    break
+
+        return parents

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_modules/pycellga/selection/tournament_selection.html b/_modules/pycellga/selection/tournament_selection.html new file mode 100644 index 0000000..6b90507 --- /dev/null +++ b/_modules/pycellga/selection/tournament_selection.html @@ -0,0 +1,379 @@ + + + + + + pycellga.selection.tournament_selection — PYCELLGA Documentation 1.0.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • pycellga.selection.tournament_selection
    • +
  • +
    • +
+
+
+
+
+ +

Source code for pycellga.selection.tournament_selection

+from typing import List
+from individual import Individual
+from selection.selection_operator import SelectionOperator
+import numpy as np
+
+
+

+[docs]
+class TournamentSelection(SelectionOperator):
+    """
+    TournamentSelection performs a tournament selection on a population of individuals
+    to select parent individuals for crossover.
+
+    Parameters
+    ----------
+    pop_list : list of Individual
+        The population of individuals to select from.
+    c : int
+        The index of the individual to start selection from.
+    K : int
+        The number of individuals to be chosen at random from neighbors.
+    """
+
+

+[docs]
+    def __init__(self, pop_list: List[Individual] = [], c: int = 0, K: int = 2):
+        """
+        Initialize the TournamentSelection object.
+
+        Parameters
+        ----------
+        pop_list : list of Individual
+            The population of individuals to select from.
+        c : int
+            The index of the individual to start selection from.
+        K : int
+            The number of individuals to be chosen at random from neighbors.
+        """
+        self.pop_list = pop_list
+        self.c = c
+        self.K = K

+
+
+

+[docs]
+    def get_parents(self) -> List[Individual]:
+        """
+        Perform the tournament selection to get parent individuals.
+
+        Returns
+        -------
+        list of Individual
+            A list containing the selected parent individuals.
+        """
+        parents = []
+        p1 = self.pop_list[self.c - 1]
+        parents.append(p1)
+        neighbors_positions = p1.neighbors_positions
+        neighbors = []
+
+        # Find neighbors in the population
+        for i in range(len(self.pop_list)):
+            if self.pop_list[i].position in neighbors_positions:
+                neighbors.append(self.pop_list[i])
+
+        tournament_selection_pool = []
+
+        # Select K individuals randomly from neighbors for the tournament
+        while len(tournament_selection_pool) < self.K:
+            index = np.random.randint(0, len(neighbors))
+            if neighbors[index] not in tournament_selection_pool:
+                tournament_selection_pool.append(neighbors[index])
+
+        # Sort the tournament selection pool by fitness value in descending order
+        tournament_selection_pool_ordered = sorted(
+            tournament_selection_pool, key=lambda x: x.fitness_value, reverse=True
+        )
+
+        # Select the individual with the highest fitness value as the second parent
+        p2 = tournament_selection_pool_ordered[0]
+        parents.append(p2)
+
+        return parents

+

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/_static/basic.css b/_static/basic.css index 7ebbd6d..f316efc 100644 --- a/_static/basic.css +++ b/_static/basic.css @@ -1,5 +1,12 @@ /* + * basic.css + * ~~~~~~~~~ + * * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * */ /* -- main layout ----------------------------------------------------------- */ @@ -108,11 +115,15 @@ img { /* -- search page ----------------------------------------------------------- */ ul.search { - margin-top: 10px; + margin: 10px 0 0 20px; + padding: 0; } ul.search li { - padding: 5px 0; + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; } ul.search li a { diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css index 88ba55b..c718cee 100644 --- a/_static/css/badge_only.css +++ b/_static/css/badge_only.css @@ -1 +1 @@ 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- - Hosted by Read the Docs - -
-
- `; - - // Inject the generated flyout into the body HTML element. - document.body.insertAdjacentHTML("beforeend", flyout); - - // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout. - document - .querySelector("#flyout-search-form") - .addEventListener("focusin", () => { - const event = new CustomEvent("readthedocs-search-show"); - document.dispatchEvent(event); - }); - }) -} - -if (themeLanguageSelector || themeVersionSelector) { - function onSelectorSwitch(event) { - const option = event.target.selectedIndex; - const item = event.target.options[option]; - window.location.href = item.dataset.url; - } - - document.addEventListener("readthedocs-addons-data-ready", function (event) { - const config = event.detail.data(); - - const versionSwitch = document.querySelector( - "div.switch-menus > div.version-switch", - ); - if (themeVersionSelector) { - let versions = config.versions.active; - if (config.versions.current.hidden || config.versions.current.type === "external") { - versions.unshift(config.versions.current); - } - const versionSelect = ` - - `; - - versionSwitch.innerHTML = versionSelect; - versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch); - } - - const languageSwitch = document.querySelector( - "div.switch-menus > div.language-switch", - ); - - if (themeLanguageSelector) { - if (config.projects.translations.length) { - // Add the current language to the options on the selector - let languages = config.projects.translations.concat( - config.projects.current, - ); - languages = languages.sort((a, b) => - a.language.name.localeCompare(b.language.name), - ); - - const languageSelect = ` - - `; - - languageSwitch.innerHTML = languageSelect; - languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch); - } - else { - languageSwitch.remove(); - } - } - }); -} - -document.addEventListener("readthedocs-addons-data-ready", function (event) { - // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav. - document - .querySelector("[role='search'] input") - .addEventListener("focusin", () => { - const event = new CustomEvent("readthedocs-search-show"); - document.dispatchEvent(event); - }); -}); \ No newline at end of file diff --git a/_static/language_data.js b/_static/language_data.js index c7fe6c6..367b8ed 100644 --- a/_static/language_data.js +++ b/_static/language_data.js @@ -1,6 +1,13 @@ /* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * */ var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; diff --git a/_static/searchtools.js b/_static/searchtools.js index 2c774d1..b08d58c 100644 --- a/_static/searchtools.js +++ b/_static/searchtools.js @@ -1,5 +1,12 @@ /* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * */ "use strict"; @@ -13,7 +20,7 @@ if (typeof Scorer === "undefined") { // and returns the new score. /* score: result => { - const [docname, title, anchor, descr, score, filename, kind] = result + const [docname, title, anchor, descr, score, filename] = result return score }, */ @@ -40,14 +47,6 @@ if (typeof Scorer === "undefined") { }; } -// Global search result kind enum, used by themes to style search results. -class SearchResultKind { - static get index() { return "index"; } - static get object() { return "object"; } - static get text() { return "text"; } - static get title() { return "title"; } -} - const _removeChildren = (element) => { while (element && element.lastChild) element.removeChild(element.lastChild); }; @@ -65,13 +64,9 @@ const _displayItem = (item, searchTerms, highlightTerms) => { const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; const contentRoot = document.documentElement.dataset.content_root; - const [docName, title, anchor, descr, score, _filename, kind] = item; + const [docName, title, anchor, descr, score, _filename] = item; let listItem = document.createElement("li"); - // Add a class representing the item's type: - // can be used by a theme's CSS selector for styling - // See SearchResultKind for the class names. - listItem.classList.add(`kind-${kind}`); let requestUrl; let linkUrl; if (docBuilder === "dirhtml") { @@ -120,10 +115,8 @@ const _finishSearch = (resultCount) => { "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." ); else - Search.status.innerText = Documentation.ngettext( - "Search finished, found one page matching the search query.", - "Search finished, found ${resultCount} pages matching the search query.", - resultCount, + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." ).replace('${resultCount}', resultCount); }; const _displayNextItem = ( @@ -145,7 +138,7 @@ const _displayNextItem = ( else _finishSearch(resultCount); }; // Helper function used by query() to order search results. -// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Each input is an array of [docname, title, anchor, descr, score, filename]. // Order the results by score (in opposite order of appearance, since the // `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. const _orderResultsByScoreThenName = (a, b) => { @@ -255,7 +248,6 @@ const Search = { searchSummary.classList.add("search-summary"); searchSummary.innerText = ""; const searchList = document.createElement("ul"); - searchList.setAttribute("role", "list"); searchList.classList.add("search"); const out = document.getElementById("search-results"); @@ -326,7 +318,7 @@ const Search = { const indexEntries = Search._index.indexentries; // Collect multiple result groups to be sorted separately and then ordered. - // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + // Each is an array of [docname, title, anchor, descr, score, filename]. const normalResults = []; const nonMainIndexResults = []; @@ -345,7 +337,6 @@ const Search = { null, score + boost, filenames[file], - SearchResultKind.title, ]); } } @@ -363,7 +354,6 @@ const Search = { null, score, filenames[file], - SearchResultKind.index, ]; if (isMain) { normalResults.push(result); @@ -485,7 +475,6 @@ const Search = { descr, score, filenames[match[0]], - SearchResultKind.object, ]); }; Object.keys(objects).forEach((prefix) => @@ -596,7 +585,6 @@ const Search = { null, score, filenames[file], - SearchResultKind.text, ]); } return results; diff --git a/citing.html b/citing.html index 8ded737..c8bfc0f 100644 --- a/citing.html +++ b/citing.html @@ -1,5 +1,3 @@ - - @@ -8,14 +6,18 @@ Citing — PYCELLGA Documentation 1.0.1 documentation - + - - - - - + + + + + + + @@ -88,17 +90,17 @@
  • Understanding Recombination
  • Recombination Examples
  • API References
    • @@ -119,7 +121,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -127,10 +132,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples @@ -120,7 +122,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -128,10 +133,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples @@ -117,7 +119,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -125,10 +130,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples
    • diff --git a/index.html b/index.html index e7d7947..5d1d9b2 100644 --- a/index.html +++ b/index.html @@ -1,5 +1,3 @@ - - @@ -8,14 +6,18 @@ PYCELLGA Documentation — PYCELLGA Documentation 1.0.1 documentation - + - - - - - + + + + + + + @@ -88,17 +90,17 @@
  • Understanding Recombination
  • Recombination Examples
  • API References
    • @@ -119,7 +121,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -127,10 +132,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples @@ -120,7 +122,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -128,10 +133,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples @@ -120,7 +122,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -128,10 +133,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples

    In Figure 4, a sample coding of the number -12345.6789 with 1 bit for the sign part, 8 bits for the exponent part and 23 bits for the decimal part, totaling 32 bits, is realized according to the IEEE-754 standard.

    -    MMCGA Illustration - +MMCGA Illustration

    Figure 4: Encoding the number according to the IEEE-754 standard.

    API References

    The following section provides the API reference for the pycellga.optimizer module.

    +
    +
    +class Result(chromosome: List[float], fitness_value: float, generation_found: int)[source]
    +

    Bases: object

    +
    +
    +__init__(chromosome: List[float], fitness_value: float, generation_found: int) → None
    +
    + +
    +
    +chromosome: List[float]
    +
    + +
    +
    +fitness_value: float
    +
    + +
    +
    +generation_found: int
    +
    + +
    + +
    +
    +alpha_cga(n_cols: int, n_rows: int, n_gen: int, ch_size: int, p_crossover: float, p_mutation: float, problem: AbstractProblem, selection: SelectionOperator, recombination: RecombinationOperator, mutation: MutationOperator, seed_par: int = None) → Result[source]
    +

    Optimize a problem using an evolutionary algorithm with an alpha-male exchange mechanism.

    +
    +
    Parameters:
    +
      +

    • n_cols (int) – Number of columns in the grid for the population.

      • +

    • n_rows (int) – Number of rows in the grid for the population.

      • +

    • n_gen (int) – Number of generations to run the optimization.

      • +

    • ch_size (int) – Size of the chromosome.

      • +

    • gen_type (GeneType) – Type of genome representation (GeneType.BINARY, GeneType.PERMUTATION, or GeneType.REAL).

      • +

    • p_crossover (float) – Probability of crossover, should be between 0 and 1.

      • +

    • p_mutation (float) – Probability of mutation, should be between 0 and 1.

      • +

    • problem (AbstractProblem) – The problem instance used to evaluate fitness.

      • +

    • selection (SelectionOperator) – Function used for selection in the evolutionary algorithm.

      • +

    • recombination (RecombinationOperator) – Function used for recombination (crossover) in the evolutionary algorithm.

      • +

    • mutation (MutationOperator) – Function used for mutation in the evolutionary algorithm.

      • +

    • mins (List[float]) – List of minimum values for each gene in the chromosome (for real value optimization).

      • +

    • maxs (List[float]) – List of maximum values for each gene in the chromosome (for real value optimization).

      • +

    • seed_par (int) – Ensures the random number generation is repeatable.

      • +
    +
    +
    Returns:
    +

    A Result object containing the best solution found, with its chromosome, fitness value, and generation.

    +
    +
    Return type:
    +

    Result

    +
    +
    +
    + +
    +
    +ccga(n_cols: int, n_rows: int, n_gen: int, ch_size: int, problem: AbstractProblem, selection: SelectionOperator) → Result[source]
    +

    Perform optimization using a Cooperative Coevolutionary Genetic Algorithm (CCGA).

    +
    +
    Parameters:
    +
      +

    • n_cols (int) – Number of columns in the grid for the population.

      • +

    • n_rows (int) – Number of rows in the grid for the population.

      • +

    • n_gen (int) – Number of generations to run the optimization.

      • +

    • ch_size (int) – Size of the chromosome.

      • +

    • gen_type (GeneType) – Type of genome representation (GeneType.BINARY, Genetype.PERMUTATION, GeneType.REAL).

      • +

    • problem (AbstractProblem) – The problem instance used to evaluate fitness.

      • +

    • selection (SelectionOperator) – Function used for selection in the evolutionary algorithm.

      • +

    • mins (List[float]) – List of minimum values for each gene in the chromosome (for real value optimization).

      • +

    • maxs (List[float]) – List of maximum values for each gene in the chromosome (for real value optimization).

      • +
    +
    +
    Returns:
    +

    A Result object containing the best solution found during the optimization process, +including its chromosome, fitness value, and generation.

    +
    +
    Return type:
    +

    Result

    +
    +
    +
    + +
    +
    +cga(n_cols: int, n_rows: int, n_gen: int, ch_size: int, p_crossover: float, p_mutation: float, problem: AbstractProblem, selection: SelectionOperator, recombination: RecombinationOperator, mutation: MutationOperator, seed_par: int = None) → Result[source]
    +

    Optimize the given problem using a genetic algorithm.

    +
    +
    Parameters:
    +
      +

    • n_cols (int) – Number of columns in the population grid.

      • +

    • n_rows (int) – Number of rows in the population grid.

      • +

    • n_gen (int) – Number of generations to evolve.

      • +

    • ch_size (int) – Size of the chromosome.

      • +

    • gen_type (str) – Type of the genome representation (e.g., ‘Binary’, ‘Permutation’, ‘Real’).

      • +

    • p_crossover (float) – Probability of crossover (between 0 and 1).

      • +

    • p_mutation (float) – Probability of mutation (between 0 and 1).

      • +

    • problem (AbstractProblem) – The problem instance used for fitness evaluation.

      • +

    • selection (SelectionOperator) – Function or class used for selecting parents.

      • +

    • recombination (RecombinationOperator) – Function or class used for recombination (crossover).

      • +

    • mutation (MutationOperator) – Function or class used for mutation.

      • +

    • mins (list[float]) – List of minimum values for each gene in the chromosome (for real value optimization).

      • +

    • maxs (list[float]) – List of maximum values for each gene in the chromosome (for real value optimization).

      • +

    • seed_par (int) – Ensures the random number generation is repeatable.

      • +
    +
    +
    Returns:
    +

    A Result object containing the best solution found, with its chromosome, fitness value, and generation.

    +
    +
    Return type:
    +

    Result

    +
    +
    +
    + +
    +
    +compete(p1: Individual, p2: Individual) → Tuple[Individual, Individual][source]
    +

    Compete between two individuals to determine the better one.

    +
    +
    Parameters:
    +
    +
    +
    Returns:
    +

    The better individual and the loser.

    +
    +
    Return type:
    +

    Tuple[Individual, Individual]

    +
    +
    +
    + +
    +
    +generate_probability_vector(mins: List[float], maxs: List[float], ntries: int) → List[float][source]
    +

    Generate a probability vector based on the given minimum and maximum values.

    +
    +
    Parameters:
    +
      +

    • mins (List[float]) – List of minimum values.

      • +

    • maxs (List[float]) – List of maximum values.

      • +

    • ntries (int) – Number of trials for generating the probability vector.

      • +
    +
    +
    Returns:
    +

    Probability vector.

    +
    +
    Return type:
    +

    List[float]

    +
    +
    +
    + +
    +
    +mcccga(n_cols: int, n_rows: int, n_gen: int, ch_size: int, problem: AbstractProblem, selection: SelectionOperator) → Result[source]
    +

    Optimize the given problem using a multi-population machine-coded compact genetic algorithm (MCCGA).

    +
    +
    Parameters:
    +
      +

    • n_cols (int) – Number of columns in the population grid.

      • +

    • n_rows (int) – Number of rows in the population grid.

      • +

    • n_gen (int) – Number of generations to evolve.

      • +

    • ch_size (int) – Size of the chromosome.

      • +

    • problem (AbstractProblem) – Problem instance for fitness evaluation.

      • +

    • selection (SelectionOperator) – Function or class used for selecting parents.

      • +
    +
    +
    Returns:
    +

    A Result instance containing the best solution found during optimization, +including its chromosome, fitness value, and generation found.

    +
    +
    Return type:
    +

    Result

    +
    +
    +
    + +
    +
    +random_vector_between(mins: List[float], maxs: List[float]) → List[float][source]
    +

    Generate a random vector of floats between the given minimum and maximum values.

    +
    +
    Parameters:
    +
      +

    • mins (List[float]) – List of minimum values.

      • +

    • maxs (List[float]) – List of maximum values.

      • +
    +
    +
    Returns:
    +

    Randomly generated vector.

    +
    +
    Return type:
    +

    List[float]

    +
    +
    +
    + +
    +
    +sample(probvector: List[float]) → List[int][source]
    +

    Sample a vector based on the provided probability vector.

    +
    +
    Parameters:
    +

    probvector (List[float]) – Probability vector for sampling.

    +
    +
    Returns:
    +

    Sampled binary vector.

    +
    +
    Return type:
    +

    List[int]

    +
    +
    +
    + +
    +
    +sync_cga(n_cols: int, n_rows: int, n_gen: int, ch_size: int, p_crossover: float, p_mutation: float, problem: Callable[[List[float]], float], selection: SelectionOperator, recombination: RecombinationOperator, mutation: MutationOperator, seed_par: int = None) → Result[source]
    +

    Optimize the given problem using a synchronous cellular genetic algorithm (Sync-CGA).

    +
    +
    Parameters:
    +
      +

    • n_cols (int) – Number of columns in the population grid.

      • +

    • n_rows (int) – Number of rows in the population grid.

      • +

    • n_gen (int) – Number of generations to evolve.

      • +

    • ch_size (int) – Size of the chromosome.

      • +

    • gen_type (str) – Type of the genome representation (e.g., ‘Binary’, ‘Permutation’, ‘Real’).

      • +

    • p_crossover (float) – Probability of crossover between parents.

      • +

    • p_mutation (float) – Probability of mutation in offspring.

      • +

    • problem (Callable[[List[float]], float]) – Function to evaluate the fitness of a solution. Takes a list of floats and returns a float.

      • +

    • selection (SelectionOperator) – Function or class used for selecting parents.

      • +

    • recombination (RecombinationOperator) – Function or class used for recombination (crossover).

      • +

    • mutation (MutationOperator) – Function or class used for mutation.

      • +

    • mins (List[float]) – List of minimum values for each gene in the chromosome (for real value optimization).

      • +

    • maxs (List[float]) – List of maximum values for each gene in the chromosome (for real value optimization).

      • +

    • seed_par (int) – Ensures the random number generation is repeatable.

      • +
    +
    +
    Returns:
    +

    A Result object containing the best solution found, with its chromosome, fitness value, and generation.

    +
    +
    Return type:
    +

    Result

    +
    +
    +
    + +
    +
    +update_vector(vector: List[float], winner: Individual, loser: Individual, pop_size: int)[source]
    +

    Update the probability vector based on the winner and loser individuals.

    +
    +
    Parameters:
    +
      +

    • vector (List[float]) – Probability vector to be updated.

      • +

    • winner (Individual) – The winning individual.

      • +

    • loser (Individual) – The losing individual.

      • +

    • pop_size (int) – Size of the population.

      • +
    +
    +
    +
    +
    diff --git a/pycellga.mutation.html b/pycellga.mutation.html index 74d7879..f612316 100644 --- a/pycellga.mutation.html +++ b/pycellga.mutation.html @@ -1,5 +1,3 @@ - - @@ -8,14 +6,18 @@ Mutation Operators — PYCELLGA Documentation 1.0.1 documentation - + - - - - - + + + + + + + @@ -89,17 +91,17 @@
  • Understanding Recombination
  • Recombination Examples
  • API References
    • @@ -120,7 +122,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -128,10 +133,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples @@ -120,7 +122,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -128,10 +133,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples @@ -120,7 +122,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -128,10 +133,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples @@ -120,7 +122,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -128,10 +133,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples @@ -120,7 +122,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
  • Single-Objective Problems @@ -128,10 +133,66 @@
  • Core Modules API Refences
    • -
  • Population Management
    • -
  • Individual Representation
    • -
  • Grid Structure
    • -
  • Byte Operators
    • +
  • Population Management +
    • +
  • Individual Representation +
    • +
  • Grid Structure +
    • +
  • Byte Operators +
  • Optimizer
  • Usage Examples @@ -120,7 +122,10 @@
  • Attributes
  • Methods
  • Example
    • -
  • API References
    • +
  • API References +
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  • Usage Examples @@ -120,7 +122,10 @@
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  • Usage Examples @@ -120,7 +122,10 @@
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  • Usage Examples