From d42dad667ce7bb2a99e1362061c556c4fdc68e03 Mon Sep 17 00:00:00 2001 From: SevgiAkten Date: Tue, 13 Aug 2024 16:35:44 +0000 Subject: [PATCH] deploy: 00ba8ea972b1f91822e9b5d0c546d888f782b8d0 --- genindex.html | 700 ------------------ modules.html | 3 - objects.inv | Bin 5924 -> 4241 bytes searchindex.js | 2 +- src.example.html | 80 -- src.html | 223 ------ src.mutation.html | 179 ----- src.neighborhoods.html | 96 --- src.problems.html | 16 - src.problems.single_objective.continuous.html | 499 ------------- ...lems.single_objective.discrete.binary.html | 189 ----- src.problems.single_objective.discrete.html | 40 - ...single_objective.discrete.permutation.html | 21 - src.problems.single_objective.html | 95 --- src.recombination.html | 242 ------ src.selection.html | 53 -- src.tests.html | 428 ----------- 17 files changed, 1 insertion(+), 2865 deletions(-) diff --git a/genindex.html b/genindex.html index 2f11489..e799bfe 100644 --- a/genindex.html +++ b/genindex.html @@ -98,324 +98,6 @@

Index

_

- diff --git a/modules.html b/modules.html index e43248b..634fc49 100644 --- a/modules.html +++ b/modules.html @@ -238,10 +238,7 @@

pycellga
  • Grid
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    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.example.example_alpha_cga', '__doc__': '\n    Example problem class to be minimized.\n\n    This class implements a simple sum of squares function with a global minimum value of 0,\n    achieved when all elements of the chromosome are equal to 0.\n    ', '__init__': <function ExampleProblem.__init__>, 'f': <function ExampleProblem.f>, '__dict__': <attribute '__dict__' of 'ExampleProblem' objects>, '__weakref__': <attribute '__weakref__' of 'ExampleProblem' objects>, '__annotations__': {}})
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    @@ -178,27 +162,11 @@

    Submodulesobject

    Example problem class to be minimized.

    This class implements a simple binary optimization problem, where the goal is to maximize the number of 1s.

    -
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    -__dict__ = mappingproxy({'__module__': 'src.example.example_ccga', '__doc__': '\n    Example problem class to be minimized.\n\n    This class implements a simple binary optimization problem, where the goal is to maximize the number of 1s.\n    ', '__init__': <function ExampleProblem.__init__>, 'f': <function ExampleProblem.f>, '__dict__': <attribute '__dict__' of 'ExampleProblem' objects>, '__weakref__': <attribute '__weakref__' of 'ExampleProblem' objects>, '__annotations__': {}})
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    @@ -246,27 +214,11 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.example.example_cga', '__doc__': '\n    Example problem class to be minimized.\n\n    This class implements a simple sum of squares function with a global minimum value of 0,\n    achieved when all elements of the chromosome are equal to 0.\n    ', '__init__': <function ExampleProblem.__init__>, 'f': <function ExampleProblem.f>, '__dict__': <attribute '__dict__' of 'ExampleProblem' objects>, '__weakref__': <attribute '__weakref__' of 'ExampleProblem' objects>, '__annotations__': {}})
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    @@ -314,27 +266,11 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.example.example_mcccga', '__doc__': '\n    Example problem class to be minimized.\n\n    This class implements a simple sum of squares function with a global minimum value of 0,\n    achieved when all elements of the chromosome are equal to 0.\n    ', '__init__': <function RealProblem.__init__>, 'f': <function RealProblem.f>, '__dict__': <attribute '__dict__' of 'RealProblem' objects>, '__weakref__': <attribute '__weakref__' of 'RealProblem' objects>, '__annotations__': {}})
    -

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    -__module__ = 'src.example.example_mcccga'
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    @@ -383,27 +319,11 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.example.example_sync_cga', '__doc__': '\n    Example problem class to be minimized.\n\n    This class implements a simple sum of squares function with a global minimum value of 0,\n    achieved when all elements of the chromosome are equal to 0.\n    ', '__init__': <function ExampleProblem.__init__>, 'f': <function ExampleProblem.f>, '__dict__': <attribute '__dict__' of 'ExampleProblem' objects>, '__weakref__': <attribute '__weakref__' of 'ExampleProblem' objects>, '__annotations__': {}})
    -

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    __init__()[source]
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    -__module__ = 'src.example.example_sync_cga'
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    diff --git a/src.html b/src.html index 67e4576..cbdbfd8 100644 --- a/src.html +++ b/src.html @@ -115,10 +115,7 @@

    SubpackagesSubmodules
  • src.example.example_alpha_cga module

    • -
    __init__(n_rows: int, n_cols: int)[source]
    @@ -1227,17 +1015,6 @@

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    -__module__ = 'src.grid'
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    diff --git a/src.mutation.html b/src.mutation.html index 675f44c..6fd82da 100644 --- a/src.mutation.html +++ b/src.mutation.html @@ -117,11 +117,6 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.mutation.bit_flip_mutation', '__doc__': '\n    BitFlipMutation performs a bit flip mutation on an individual in a Genetic Algorithm.\n\n    Parameters\n    ----------\n    mutation_cand : Individual, optional\n        The candidate individual to be mutated (default is None).\n    problem : AbstractProblem, optional\n        The problem instance that provides the fitness function (default is None).\n    ', '__init__': <function BitFlipMutation.__init__>, 'mutate': <function BitFlipMutation.mutate>, '__annotations__': {}})
    -

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    __init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
    @@ -136,17 +131,6 @@

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    @@ -182,16 +166,6 @@

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    -__dict__ = mappingproxy({'__module__': 'src.mutation.byte_mutation', '__doc__': "\n    ByteMutation operator defined in (Satman, 2013). ByteMutation performs a byte-wise mutation \n    on an individual's chromosome in a Genetic Algorithm.\n\n    Parameters\n    ----------\n    mutation_cand : Individual, optional\n        The candidate individual to be mutated (default is None).\n    problem : AbstractProblem, optional\n        The problem instance that provides the fitness function (default is None).\n    ", '__init__': <function ByteMutation.__init__>, 'mutate': <function ByteMutation.mutate>, '__annotations__': {}})
    -
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    __init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
    @@ -206,17 +180,6 @@

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    mutate() → Individual[source]
    @@ -252,16 +215,6 @@

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    -__annotations__ = {}
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    -__dict__ = mappingproxy({'__module__': 'src.mutation.byte_mutation_random', '__doc__': "\n    ByteMutationRandom operator defined in (Satman, 2013). ByteMutationRandom performs \n    a random byte mutation on an individual's chromosome in a Genetic Algorithm.\n\n    Parameters\n    ----------\n    mutation_cand : Individual, optional\n        The candidate individual to be mutated (default is None).\n    problem : AbstractProblem, optional\n        The problem instance that provides the fitness function (default is None).\n    ", '__init__': <function ByteMutationRandom.__init__>, 'mutate': <function ByteMutationRandom.mutate>, '__annotations__': {}})
    -
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    __init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
    @@ -276,17 +229,6 @@

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    @@ -321,16 +263,6 @@

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    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.mutation.float_uniform_mutation', '__doc__': "\n    FloatUniformMutation performs a uniform mutation on an individual's chromosome in a Genetic Algorithm.\n\n    Parameters\n    ----------\n    mutation_cand : Individual, optional\n        The candidate individual to be mutated (default is None).\n    problem : AbstractProblem, optional\n        The problem instance that provides the fitness function (default is None).\n    ", '__init__': <function FloatUniformMutation.__init__>, 'mutate': <function FloatUniformMutation.mutate>, '__annotations__': {}})
    -
    -
    __init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
    @@ -345,17 +277,6 @@

    Submodules -
    -__module__ = 'src.mutation.float_uniform_mutation'
    -

    - -
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    @@ -390,16 +311,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.mutation.insertion_mutation', '__doc__': "\n    InsertionMutation performs an insertion mutation on an individual's chromosome in a Genetic Algorithm.\n\n    Parameters\n    ----------\n    mutation_cand : Individual, optional\n        The candidate individual to be mutated (default is None).\n    problem : AbstractProblem, optional\n        The problem instance that provides the fitness function (default is None).\n    ", '__init__': <function InsertionMutation.__init__>, 'mutate': <function InsertionMutation.mutate>, '__annotations__': {}})
    -
    -
    __init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
    @@ -414,17 +325,6 @@

    Submodules -
    -__module__ = 'src.mutation.insertion_mutation'
    -

    - -
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    mutate() → Individual[source]
    @@ -449,22 +349,6 @@

    Submodules class src.mutation.mutation_operator.MutationOperator[source]

    Bases: object

    -
    -
    -__dict__ = mappingproxy({'__module__': 'src.mutation.mutation_operator', 'mutate': <function MutationOperator.mutate>, '__dict__': <attribute '__dict__' of 'MutationOperator' objects>, '__weakref__': <attribute '__weakref__' of 'MutationOperator' objects>, '__doc__': None, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.mutation.mutation_operator'
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    mutate()[source]
    @@ -488,16 +372,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.mutation.shuffle_mutation', '__doc__': "\n    ShuffleMutation performs a shuffle mutation on an individual's chromosome in a Genetic Algorithm.\n\n    Parameters\n    ----------\n    mutation_cand : Individual, optional\n        The candidate individual to be mutated (default is None).\n    problem : AbstractProblem, optional\n        The problem instance that provides the fitness function (default is None).\n    ", '__init__': <function ShuffleMutation.__init__>, 'mutate': <function ShuffleMutation.mutate>, '__annotations__': {}})
    -
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    __init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
    @@ -512,17 +386,6 @@

    Submodules -
    -__module__ = 'src.mutation.shuffle_mutation'
    -

    - -
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    @@ -556,16 +419,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.mutation.swap_mutation', '__doc__': "\n    SwapMutation performs a swap mutation on an individual's chromosome in a Genetic Algorithm.\n\n    Parameters\n    ----------\n    mutation_cand : Individual, optional\n        The candidate individual to be mutated (default is None).\n    problem : AbstractProblem, optional\n        The problem instance that provides the fitness function (default is None).\n    ", '__init__': <function SwapMutation.__init__>, 'mutate': <function SwapMutation.mutate>, '__annotations__': {}})
    -
    -
    __init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
    @@ -580,17 +433,6 @@

    Submodules -
    -__module__ = 'src.mutation.swap_mutation'
    -

    - -
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    @@ -624,16 +466,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.mutation.two_opt_mutation', '__doc__': "\n    TwoOptMutation performs a 2-opt mutation on an individual's chromosome in a Genetic Algorithm.\n\n    Parameters\n    ----------\n    mutation_cand : Individual, optional\n        The candidate individual to be mutated (default is None).\n    problem : AbstractProblem, optional\n        The problem instance that provides the fitness function (default is None).\n    ", '__init__': <function TwoOptMutation.__init__>, 'mutate': <function TwoOptMutation.mutate>, '__annotations__': {}})
    -
    -
    __init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
    @@ -648,17 +480,6 @@

    Submodules -
    -__module__ = 'src.mutation.two_opt_mutation'
    -

    - -
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    diff --git a/src.neighborhoods.html b/src.neighborhoods.html index 1d2ab85..97d3e29 100644 --- a/src.neighborhoods.html +++ b/src.neighborhoods.html @@ -119,11 +119,6 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.neighborhoods.compact_13', '__doc__': '\n    Compact13 calculates the positions of the 12 neighbors in a 2D grid for a given position,\n    considering wrapping at the grid edges.\n\n    Parameters\n    ----------\n    position : tuple\n        The (x, y) position of the point whose neighbors are to be calculated.\n    n_rows : int\n        The number of rows in the grid.\n    n_cols : int\n        The number of columns in the grid.\n    ', '__init__': <function Compact13.__init__>, 'calculate_neighbors_positions': <function Compact13.calculate_neighbors_positions>, '__dict__': <attribute '__dict__' of 'Compact13' objects>, '__weakref__': <attribute '__weakref__' of 'Compact13' objects>, '__annotations__': {}})
    -

    -
    __init__(position, n_rows, n_cols)[source]
    @@ -139,17 +134,6 @@

    Submodules -
    -__module__ = 'src.neighborhoods.compact_13'
    -

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    @@ -185,11 +169,6 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.neighborhoods.compact_21', '__doc__': '\n    Compact21 calculates the positions of the 20 neighbors in a 2D grid for a given position,\n    considering wrapping at the grid edges.\n\n    Parameters\n    ----------\n    position : tuple\n        The (x, y) position of the point whose neighbors are to be calculated.\n    n_rows : int\n        The number of rows in the grid.\n    n_cols : int\n        The number of columns in the grid.\n    ', '__init__': <function Compact21.__init__>, 'calculate_neighbors_positions': <function Compact21.calculate_neighbors_positions>, '__dict__': <attribute '__dict__' of 'Compact21' objects>, '__weakref__': <attribute '__weakref__' of 'Compact21' objects>, '__annotations__': {}})
    -

    -
    __init__(position, n_rows, n_cols)[source]
    @@ -205,17 +184,6 @@

    Submodules -
    -__module__ = 'src.neighborhoods.compact_21'
    -

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    calculate_neighbors_positions() → list[source]
    @@ -251,11 +219,6 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.neighborhoods.compact_25', '__doc__': '\n    Compact25 calculates the positions of the 24 neighbors in a 2D grid for a given position,\n    considering wrapping at the grid edges.\n\n    Parameters\n    ----------\n    position : tuple\n        The (x, y) position of the point whose neighbors are to be calculated.\n    n_rows : int\n        The number of rows in the grid.\n    n_cols : int\n        The number of columns in the grid.\n    ', '__init__': <function Compact25.__init__>, 'calculate_neighbors_positions': <function Compact25.calculate_neighbors_positions>, '__dict__': <attribute '__dict__' of 'Compact25' objects>, '__weakref__': <attribute '__weakref__' of 'Compact25' objects>, '__annotations__': {}})
    -

    -
    __init__(position, n_rows, n_cols)[source]
    @@ -271,17 +234,6 @@

    Submodules -
    -__module__ = 'src.neighborhoods.compact_25'
    -

    - -
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    -
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    calculate_neighbors_positions() → list[source]
    @@ -317,11 +269,6 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.neighborhoods.compact_9', '__doc__': '\n    Compact9 calculates the positions of the 8 neighbors in a 2D grid for a given position,\n    considering wrapping at the grid edges.\n\n    Parameters\n    ----------\n    position : tuple\n        The (x, y) position of the point whose neighbors are to be calculated.\n    n_rows : int\n        The number of rows in the grid.\n    n_cols : int\n        The number of columns in the grid.\n    ', '__init__': <function Compact9.__init__>, 'calculate_neighbors_positions': <function Compact9.calculate_neighbors_positions>, '__dict__': <attribute '__dict__' of 'Compact9' objects>, '__weakref__': <attribute '__weakref__' of 'Compact9' objects>, '__annotations__': {}})
    -

    -
    __init__(position, n_rows, n_cols)[source]
    @@ -337,17 +284,6 @@

    Submodules -
    -__module__ = 'src.neighborhoods.compact_9'
    -

    - -
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    -
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    calculate_neighbors_positions() → list[source]
    @@ -383,11 +319,6 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.neighborhoods.linear_5', '__doc__': '\n    Linear5 calculates the positions of the 4 neighbors in a 2D grid for a given position,\n    considering wrapping at the grid edges.\n\n    Parameters\n    ----------\n    position : tuple\n        The (x, y) position of the point whose neighbors are to be calculated.\n    n_rows : int\n        The number of rows in the grid.\n    n_cols : int\n        The number of columns in the grid.\n    ', '__init__': <function Linear5.__init__>, 'calculate_neighbors_positions': <function Linear5.calculate_neighbors_positions>, '__dict__': <attribute '__dict__' of 'Linear5' objects>, '__weakref__': <attribute '__weakref__' of 'Linear5' objects>, '__annotations__': {}})
    -

    -
    __init__(position, n_rows, n_cols)[source]
    @@ -403,17 +334,6 @@

    Submodules -
    -__module__ = 'src.neighborhoods.linear_5'
    -

    - -
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    list of weak references to the object

    -
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    calculate_neighbors_positions() → list[source]
    @@ -449,11 +369,6 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.neighborhoods.linear_9', '__doc__': '\n    Linear9 calculates the positions of the 8 neighbors in a 2D grid for a given position,\n    considering wrapping at the grid edges.\n\n    Parameters\n    ----------\n    position : tuple\n        The (x, y) position of the point whose neighbors are to be calculated.\n    n_rows : int\n        The number of rows in the grid.\n    n_cols : int\n        The number of columns in the grid.\n    ', '__init__': <function Linear9.__init__>, 'calculate_neighbors_positions': <function Linear9.calculate_neighbors_positions>, '__dict__': <attribute '__dict__' of 'Linear9' objects>, '__weakref__': <attribute '__weakref__' of 'Linear9' objects>, '__annotations__': {}})
    -

    -
    __init__(position, n_rows, n_cols)[source]
    @@ -469,17 +384,6 @@

    Submodules -
    -__module__ = 'src.neighborhoods.linear_9'
    -

    - -
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    diff --git a/src.problems.html b/src.problems.html index 9b9ec13..e44badb 100644 --- a/src.problems.html +++ b/src.problems.html @@ -163,22 +163,6 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.problems.abstract_problem', '__doc__': '\n    An abstract base class for optimization problems.\n\n    Methods\n    -------\n    f(x)\n        Evaluates the fitness of a given solution x.\n    ', 'f': <function AbstractProblem.f>, '__dict__': <attribute '__dict__' of 'AbstractProblem' objects>, '__weakref__': <attribute '__weakref__' of 'AbstractProblem' objects>, '__annotations__': {}})
    -

    - -
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    -__module__ = 'src.problems.abstract_problem'
    -
    - -
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    list of weak references to the object

    -
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    f(x)[source]
    diff --git a/src.problems.single_objective.continuous.html b/src.problems.single_objective.continuous.html index a00840e..28e63d5 100644 --- a/src.problems.single_objective.continuous.html +++ b/src.problems.single_objective.continuous.html @@ -128,22 +128,6 @@

    SubmodulesNotes

    -32.768 ≤ xi ≤ 32.768 Global minimum at f(0, 0) = 0

    -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.ackley', '__doc__': '\n    Ackley function implementation for optimization problems.\n\n    The Ackley function is widely used for testing optimization algorithms.\n    It has a nearly flat outer region and a large hole at the center.\n    The function is usually evaluated on the hypercube x_i ∈ [-32.768, 32.768], for all i = 1, 2, ..., d.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Ackley function value for a given list of variables.\n\n    Notes\n    -----\n    -32.768 ≤ xi ≤ 32.768\n    Global minimum at f(0, 0) = 0\n    ', 'f': <function Ackley.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.ackley'
    -
    - -
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    -

    list of weak references to the object

    -
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    f(x: list) → float[source]
    @@ -187,27 +171,6 @@

    SubmodulesNotes

    -100 ≤ xi ≤ 100 for i = 1,…,n Global minimum at f(0,…,0) = 0

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.bentcigar', '__doc__': '\n    Bentcigar function implementation for optimization problems.\n\n    The Bentcigar function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-100, 100], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(X: list) -> float\n        Calculates the Bentcigar function value for a given list of variables.\n\n    Notes\n    -----\n    -100 ≤ xi ≤ 100 for i = 1,…,n\n    Global minimum at f(0,...,0) = 0\n    ', 'f': <function Bentcigar.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.bentcigar'
    -
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    list of weak references to the object

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    f(X: list) → float[source]
    @@ -251,27 +214,6 @@

    SubmodulesNotes

    -15 ≤ xi ≤ 15 for i = 1,…,n Global minimum at f(0,…,0) = 0

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.bohachevsky', '__doc__': '\n    Bohachevsky function implementation for optimization problems.\n\n    The Bohachevsky function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-15, 15], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Bohachevsky function value for a given list of variables.\n\n    Notes\n    -----\n    -15 ≤ xi ≤ 15 for i = 1,…,n\n    Global minimum at f(0,...,0) = 0\n    ', 'f': <function Bohachevsky.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.bohachevsky'
    -
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    f(x: list) → float[source]
    @@ -315,27 +257,6 @@

    SubmodulesNotes

    -30 ≤ x, y ≤ 30 Global minimum at f(5.90133, 0.5) = −43.3159

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.chichinadze', '__doc__': '\n    Chichinadze function implementation for optimization problems.\n\n    The Chichinadze function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x, y ∈ [-30, 30].\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(X: list) -> float\n        Calculates the Chichinadze function value for a given list of variables.\n\n    Notes\n    -----\n    -30 ≤ x, y ≤ 30\n    Global minimum at f(5.90133, 0.5) = −43.3159\n    ', 'f': <function Chichinadze.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.chichinadze'
    -
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    list of weak references to the object

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    f(X: list) → float[source]
    @@ -371,27 +292,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.dropwave', '__doc__': '\n    Dropwave function for optimization problems.\n\n    The Dropwave function is a multimodal function commonly used as a performance test problem for optimization algorithms.\n    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.\n\n    Methods\n    -------\n    f(x: list) -> float\n        Computes the value of the Dropwave function at a given point x.\n    ', 'f': <function Dropwave.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.dropwave'
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    f(x: list) → float[source]
    @@ -450,27 +350,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.fms', '__doc__': '\n    Fms function implementation for optimization problems.\n\n    The Fms function is used for testing optimization algorithms, specifically those dealing with frequency modulation sound.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Fms function value for a given list of variables.\n\n    Notes\n    -----\n    -6.4 ≤ xi ≤ 6.35\n    Length of chromosomes = 6\n    Maximum Fitness Value = 0.01\n    Maximum Fitness Value Error = 10^-2\n    ', 'f': <function Fms.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.fms'
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    f(x: list) → float[source]
    @@ -509,27 +388,6 @@

    SubmodulesNotes

    -600 ≤ xi ≤ 600 for i = 1,…,n Global minimum at f(0,…,0) = 0

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.griewank', '__doc__': '\n    Griewank function implementation for optimization problems.\n\n    The Griewank function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-600, 600], for all i = 1, 2, ..., n.\n\n    Methods\n    -------\n    f(X: list) -> float\n        Calculates the Griewank function value for a given list of variables.\n\n    Notes\n    -----\n    -600 ≤ xi ≤ 600 for i = 1,…,n\n    Global minimum at f(0,...,0) = 0\n    ', 'f': <function Griewank.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.griewank'
    -
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    @@ -573,27 +431,6 @@

    SubmodulesNotes

    -10 ≤ xi ≤ 10 for i = 1,…,n Global minimum at f(0,…,0) = 0

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.holzman', '__doc__': '\n    Holzman function implementation for optimization problems.\n\n    The Holzman function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-10, 10], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Holzman function value for a given list of variables.\n\n    Notes\n    -----\n    -10 ≤ xi ≤ 10 for i = 1,…,n\n    Global minimum at f(0,...,0) = 0\n    ', 'f': <function Holzman.f>, '__annotations__': {}})
    -
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    @@ -637,27 +474,6 @@

    SubmodulesNotes

    -10 ≤ xi ≤ 10 for i = 1,…,n Global minimum at f(1,1) = 0

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.levy', '__doc__': '\n    Levy function implementation for optimization problems.\n\n    The Levy function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-10, 10], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Levy function value for a given list of variables.\n\n    Notes\n    -----\n    -10 ≤ xi ≤ 10 for i = 1,…,n\n    Global minimum at f(1,1) = 0\n    ', 'f': <function Levy.f>, '__annotations__': {}})
    -
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    -__module__ = 'src.problems.single_objective.continuous.levy'
    -
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    @@ -701,27 +517,6 @@

    SubmodulesNotes

    -10 ≤ xi ≤ 10 for i = 1,…,n Global minimum at f(0,…,0) = 0

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.matyas', '__doc__': '\n    Matyas function implementation for optimization problems.\n\n    The Matyas function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-10, 10], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(X: list) -> float\n        Calculates the Matyas function value for a given list of variables.\n\n    Notes\n    -----\n    -10 ≤ xi ≤ 10 for i = 1,…,n\n    Global minimum at f(0,...,0) = 0\n    ', 'f': <function Matyas.f>, '__annotations__': {}})
    -
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    f(X: list) → float[source]
    @@ -765,27 +560,6 @@

    SubmodulesNotes

    -5.0 ≤ xi ≤ 15.0 Global minimum at f(5, 7, 9, 3, 2) = 0

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.pow', '__doc__': '\n    Pow function implementation for optimization problems.\n\n    The Pow function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-5.0, 15.0].\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Pow function value for a given list of variables.\n\n    Notes\n    -----\n    -5.0 ≤ xi ≤ 15.0\n    Global minimum at f(5, 7, 9, 3, 2) = 0\n    ', 'f': <function Pow.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.pow'
    -
    - -
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    -__weakref__
    -

    list of weak references to the object

    -
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    @@ -829,27 +603,6 @@

    SubmodulesNotes

    -4 ≤ xi ≤ 5 for i = 1,…,n Global minimum at f(0,….,0) = 0

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.powell', '__doc__': '\n    Powell function implementation for optimization problems.\n\n    The Powell function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-4, 5], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Powell function value for a given list of variables.\n\n    Notes\n    -----\n    -4 ≤ xi ≤ 5 for i = 1,…,n\n    Global minimum at f(0,....,0) = 0\n    ', 'f': <function Powell.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.powell'
    -
    - -
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    -__weakref__
    -

    list of weak references to the object

    -
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    @@ -893,27 +646,6 @@

    SubmodulesNotes

    -5.12 ≤ xi ≤ 5.12 for i = 1,…,n Global minimum at f(0,…,0) = 0

    -
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    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.rastrigin', '__doc__': '\n    Rastrigin function implementation for optimization problems.\n\n    The Rastrigin function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-5.12, 5.12], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Rastrigin function value for a given list of variables.\n\n    Notes\n    -----\n    -5.12 ≤ xi ≤ 5.12 for i = 1,…,n\n    Global minimum at f(0,...,0) = 0\n    ', 'f': <function Rastrigin.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.rastrigin'
    -
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    -__weakref__
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    list of weak references to the object

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    @@ -957,27 +689,6 @@

    SubmodulesNotes

    -5 ≤ xi ≤ 10 for i = 1,…,n Global minimum at f(1,…,1) = 0

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.rosenbrock', '__doc__': '\n    Rosenbrock function implementation for optimization problems.\n\n    The Rosenbrock function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-5, 10], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Rosenbrock function value for a given list of variables.\n\n    Notes\n    -----\n    -5 ≤ xi ≤ 10 for i = 1,…,n\n    Global minimum at f(1,...,1) = 0\n    ', 'f': <function Rosenbrock.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.rosenbrock'
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    @@ -1021,27 +732,6 @@

    SubmodulesNotes

    -100 ≤ xi ≤ 100 for i = 1,…,n Global minimum at f(0,….,0) = 0

    -
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    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.rothellipsoid', '__doc__': '\n    Rotated Hyper-Ellipsoid function implementation for optimization problems.\n\n    The Rotated Hyper-Ellipsoid function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-100, 100], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Rotated Hyper-Ellipsoid function value for a given list of variables.\n\n    Notes\n    -----\n    -100 ≤ xi ≤ 100 for i = 1,…,n\n    Global minimum at f(0,....,0) = 0\n    ', 'f': <function Rothellipsoid.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.rothellipsoid'
    -
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    list of weak references to the object

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    @@ -1077,27 +767,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.schaffer', '__doc__': "\n    Schaffer's Function.\n\n    This class implements the Schaffer's function, which is a common benchmark problem for optimization algorithms.\n    The function is defined over a multidimensional input and is used to test the performance of optimization methods.\n\n    Methods\n    -------\n    f(X: list) -> float\n        Calculates the value of the Schaffer's function for a given list of input variables.\n    ", 'f': <function Schaffer.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.schaffer'
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    list of weak references to the object

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    @@ -1154,27 +823,6 @@

    SubmodulesNotes

    -100 ≤ xi ≤ 100 for i = 1,…,n Global minimum at f(0,…,0) = 0

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.schaffer2', '__doc__': '\n    Modified Schaffer function #2 implementation for optimization problems.\n\n    The Modified Schaffer function #2 is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-100, 100], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(X: list) -> float\n        Calculates the Modified Schaffer function #2 value for a given list of variables.\n\n    Notes\n    -----\n    -100 ≤ xi ≤ 100 for i = 1,…,n\n    Global minimum at f(0,...,0) = 0\n    ', 'f': <function Schaffer2.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.schaffer2'
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    @@ -1218,27 +866,6 @@

    SubmodulesNotes

    -500 ≤ xi ≤ 500 for i = 1,…,n Global minimum at f(420.9687,…,420.9687) = 0

    -
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    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.schwefel', '__doc__': '\n    Schwefel function implementation for optimization problems.\n\n    The Schwefel function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-500, 500], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Schwefel function value for a given list of variables.\n\n    Notes\n    -----\n    -500 ≤ xi ≤ 500 for i = 1,…,n\n    Global minimum at f(420.9687,…,420.9687) = 0\n    ', 'f': <function Schwefel.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.schwefel'
    -
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    list of weak references to the object

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    @@ -1282,27 +909,6 @@

    SubmodulesNotes

    -5.12 ≤ xi ≤ 5.12 for i = 1,…,n Global minimum at f(0,…,0) = 0

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.sphere', '__doc__': '\n    Sphere function implementation for optimization problems.\n\n    The Sphere function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-5.12, 5.12], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Sphere function value for a given list of variables.\n\n    Notes\n    -----\n    -5.12 ≤ xi ≤ 5.12 for i = 1,…,n\n    Global minimum at f(0,...,0) = 0\n    ', 'f': <function Sphere.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.sphere'
    -
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    list of weak references to the object

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    @@ -1346,27 +952,6 @@

    SubmodulesNotes

    -5 ≤ xi ≤ 5 for i = 1,…,n Global minimum at f(−2.903534, −2.903534) = −78.332

    -
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    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.styblinskitang', '__doc__': '\n    Styblinski-Tang function implementation for optimization problems.\n\n    The Styblinski-Tang function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-5, 5], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Styblinski-Tang function value for a given list of variables.\n\n    Notes\n    -----\n    -5 ≤ xi ≤ 5 for i = 1,…,n\n    Global minimum at f(−2.903534, −2.903534) = −78.332\n    ', 'f': <function StyblinskiTang.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.styblinskitang'
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    @@ -1394,27 +979,6 @@

    Submodulesclass src.problems.single_objective.continuous.sumofdifferentpowers.Sumofdifferentpowers[source]

    Bases: AbstractProblem

    Sum of Different Powers function implementation for optimization problems.

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.sumofdifferentpowers', '__doc__': '\n    Sum of Different Powers function implementation for optimization problems.\n    ', 'f': <function Sumofdifferentpowers.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.sumofdifferentpowers'
    -
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    list of weak references to the object

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    @@ -1458,27 +1022,6 @@

    SubmodulesNotes

    -5 ≤ xi ≤ 5 for i = 1,…,n Global minimum at f(0,..,0) = 0

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.threehumps', '__doc__': '\n    Three Hump Camel function implementation for optimization problems.\n\n    The Three Hump Camel function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-5, 5], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Three Hump Camel function value for a given list of variables.\n\n    Notes\n    -----\n    -5 ≤ xi ≤ 5 for i = 1,…,n\n    Global minimum at f(0,..,0) = 0\n    ', 'f': <function Threehumps.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.threehumps'
    -
    - -
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    -__weakref__
    -

    list of weak references to the object

    -
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    f(x: list) → float[source]
    @@ -1522,27 +1065,6 @@

    SubmodulesNotes

    -5 ≤ xi ≤ 10 for i = 1,…,n Global minimum at f(0,..,0) = 0

    -
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    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.zakharov', '__doc__': '\n    Zakharov function implementation for optimization problems.\n\n    The Zakharov function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-5, 10], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Zakharov function value for a given list of variables.\n\n    Notes\n    -----\n    -5 ≤ xi ≤ 10 for i = 1,…,n\n    Global minimum at f(0,..,0) = 0\n    ', 'f': <function Zakharov.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.continuous.zakharov'
    -
    - -
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    -__weakref__
    -

    list of weak references to the object

    -
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    @@ -1586,27 +1108,6 @@

    SubmodulesNotes

    -5 ≤ xi ≤ 5 for i = 1,…,n Global minimum at f(−0.0299, 0) = −0.003791

    -
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    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.continuous.zettle', '__doc__': '\n    Zettle function implementation for optimization problems.\n\n    The Zettle function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-5, 5], for all i = 1, 2, ..., n.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Zettle function value for a given list of variables.\n\n    Notes\n    -----\n    -5 ≤ xi ≤ 5 for i = 1,…,n\n    Global minimum at f(−0.0299, 0) = −0.003791\n    ', 'f': <function Zettle.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.continuous.zettle'
    -
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    -__weakref__
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    list of weak references to the object

    -
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    f(x: list) → float[source]
    diff --git a/src.problems.single_objective.discrete.binary.html b/src.problems.single_objective.discrete.binary.html index 4d48849..23ad7c6 100644 --- a/src.problems.single_objective.discrete.binary.html +++ b/src.problems.single_objective.discrete.binary.html @@ -128,27 +128,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.binary.count_sat', '__doc__': '\n    CountSat function implementation for optimization problems.\n\n    The CountSat function is used for testing optimization algorithms, particularly those involving satisfiability problems.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the CountSat function value for a given list of variables.\n\n    Notes\n    -----\n    Length of chromosomes = 20\n    Maximum Fitness Value = 6860\n    Maximum Fitness Value (normalized) = 1\n    ', 'f': <function CountSat.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.discrete.binary.count_sat'
    -
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    list of weak references to the object

    -
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    @@ -192,27 +171,6 @@

    SubmodulesNotes

    Length of chromosomes = 144 Maximum Fitness Value = 0.0674

    -
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    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.binary.ecc', '__doc__': '\n    Error Correcting Codes Design Problem (ECC) function implementation for optimization problems.\n\n    The ECC function is used for testing optimization algorithms, particularly those involving \n    error-correcting codes.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the ECC function value for a given list of variables.\n\n    Notes\n    -----\n    Length of chromosomes = 144\n    Maximum Fitness Value = 0.0674\n    ', 'f': <function Ecc.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.discrete.binary.ecc'
    -
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    list of weak references to the object

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    @@ -256,27 +214,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.binary.fms', '__doc__': '\n    Frequency Modulation Sound (FMS) function implementation for optimization problems.\n\n    The FMS function is used for testing optimization algorithms, particularly those involving frequency modulation sound.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the FMS function value for a given list of variables.\n\n    Notes\n    -----\n    Length of chromosomes = 192\n    Maximum Fitness Value = 0.01\n    Maximum Fitness Value Error = 10^-2\n    ', 'f': <function Fms.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.discrete.binary.fms'
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    @@ -318,27 +255,6 @@

    SubmodulesNotes

    Length of chromosomes = 100 Maximum Fitness Value = 1077.0

    -
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    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.binary.maxcut100', '__doc__': '\n    A class used to represent the Maximum Cut (MAXCUT) function for 100 nodes.\n    \n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the fitness value of a given chromosome.\n\n    Notes\n    -----\n    Length of chromosomes = 100\n    Maximum Fitness Value = 1077.0\n    ', 'f': <function Maxcut100.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.discrete.binary.maxcut100'
    -
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    @@ -381,27 +297,6 @@

    SubmodulesNotes

    Length of chromosomes = 20 Maximum Fitness Value = 10.119812

    -
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    -__annotations__ = {}
    -
    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.binary.maxcut20_01', '__doc__': '\n    Maximum Cut (MAXCUT) function implementation for optimization problems.\n\n    The MAXCUT function is used for testing optimization algorithms, particularly those involving maximum cut problems.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the MAXCUT function value for a given list of variables.\n\n    Notes\n    -----\n    Length of chromosomes = 20\n    Maximum Fitness Value = 10.119812\n    ', 'f': <function Maxcut20_01.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.discrete.binary.maxcut20_01'
    -
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    @@ -444,27 +339,6 @@

    SubmodulesNotes

    Length of chromosomes = 20 Maximum Fitness Value = 56.740064

    -
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    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.binary.maxcut20_09', '__doc__': '\n    Maximum Cut (MAXCUT) function implementation for optimization problems.\n\n    The MAXCUT function is used for testing optimization algorithms, particularly those involving maximum cut problems.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the MAXCUT function value for a given list of variables.\n\n    Notes\n    -----\n    Length of chromosomes = 20\n    Maximum Fitness Value = 56.740064\n    ', 'f': <function Maxcut20_09.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.discrete.binary.maxcut20_09'
    -
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    @@ -509,27 +383,6 @@

    SubmodulesNotes

    # Length of chromosomes = 240 # Maximum Fitness Value = 40

    -
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    -__annotations__ = {}
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.binary.mmdp', '__doc__': '\n    Represents the Massively Multimodal Deceptive Problem (MMDP).\n\n    The MMDP is designed to deceive genetic algorithms by having multiple local \n    optima. The problem is characterized by a chromosome length of 240 and a \n    maximum fitness value of 40.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Evaluates the fitness of a given chromosome.\n    \n    Notes\n    -----\n    # Length of chromosomes = 240\n    # Maximum Fitness Value = 40\n    ', 'f': <function Mmdp.f>, '__annotations__': {}})
    -
    - -
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    -__module__ = 'src.problems.single_objective.discrete.binary.mmdp'
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    @@ -574,27 +427,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
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    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.binary.one_max', '__doc__': '\n    Represents the OneMax problem.\n\n    The OneMax problem is a simple genetic algorithm benchmark problem \n    where the fitness of a chromosome is the sum of its bits.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Evaluates the fitness of a given chromosome.\n    ', 'f': <function OneMax.f>, '__annotations__': {}})
    -
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    -__module__ = 'src.problems.single_objective.discrete.binary.one_max'
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    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x) → float[source]
    @@ -640,27 +472,6 @@

    SubmodulesNotes

    # Length of chromosomes = 100 # Maximum Fitness Value = 1.0

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.binary.peak', '__doc__': '\n    Represents the Peak problem.\n\n    The Peak problem evaluates the fitness of a chromosome based on its \n    distance to a set of target peaks.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Evaluates the fitness of a given chromosome.\n    \n    Notes\n    -----\n    # Length of chromosomes = 100\n    # Maximum Fitness Value = 1.0\n    ', 'f': <function Peak.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.discrete.binary.peak'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    diff --git a/src.problems.single_objective.discrete.html b/src.problems.single_objective.discrete.html index e00151c..0d8fe16 100644 --- a/src.problems.single_objective.discrete.html +++ b/src.problems.single_objective.discrete.html @@ -111,10 +111,6 @@

    SubpackagesCountSat @@ -124,10 +120,6 @@

    SubpackagesEcc @@ -137,10 +129,6 @@

    SubpackagesFms @@ -150,10 +138,6 @@

    SubpackagesMaxcut100 @@ -163,10 +147,6 @@

    SubpackagesMaxcut20_01 @@ -176,10 +156,6 @@

    SubpackagesMaxcut20_09 @@ -189,10 +165,6 @@

    SubpackagesMmdp @@ -202,10 +174,6 @@

    SubpackagesOneMax @@ -215,10 +183,6 @@

    SubpackagesPeak @@ -231,10 +195,6 @@

    SubpackagesSubmodules
  • src.problems.single_objective.discrete.permutation.tsp module

    • - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.problems.single_objective.discrete.permutation.tsp', '__doc__': '\n    Represents the Traveling Salesman Problem (TSP).\n\n    This class solves the TSP using geographical distances (GEO) for node coordinates.\n\n    Notes\n    ----------\n    #### burma14.tsp ########################################\n    # EDGE_WEIGHT_TYPE: GEO,   use gographical_dist function\n    # Length of chromosomes = 14\n    # Known Best Route = []\n    # Minumum Fitness Value = 3323\n    #########################################################\n    ', 'f': <function Tsp.f>, 'euclidean_dist': <function Tsp.euclidean_dist>, 'gographical_dist': <function Tsp.gographical_dist>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.problems.single_objective.discrete.permutation.tsp'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    euclidean_dist(a: list, b: list) → float[source]
    diff --git a/src.problems.single_objective.html b/src.problems.single_objective.html index 1f413d5..bd51792 100644 --- a/src.problems.single_objective.html +++ b/src.problems.single_objective.html @@ -110,9 +110,6 @@

    SubpackagesAckley @@ -122,10 +119,6 @@

    SubpackagesBentcigar @@ -135,10 +128,6 @@

    SubpackagesBohachevsky @@ -148,10 +137,6 @@

    SubpackagesChichinadze @@ -160,10 +145,6 @@

    Subpackagessrc.problems.single_objective.continuous.dropwave module

    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -137,17 +132,6 @@

    Submodules -
    -__module__ = 'src.recombination.arithmetic_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_recombinations() → List[Individual][source]
    @@ -181,16 +165,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.blxalpha_crossover', '__doc__': '\n    BlxalphaCrossover performs BLX-alpha crossover on a pair of parent individuals\n    to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function BlxalphaCrossover.__init__>, 'combine': <function BlxalphaCrossover.combine>, 'get_recombinations': <function BlxalphaCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -205,17 +179,6 @@

    Submodules -
    -__module__ = 'src.recombination.blxalpha_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
    @@ -270,16 +233,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.byte_one_point_crossover', '__doc__': '\n    ByteOnePointCrossover operator defined in (Satman, 2013). ByteOnePointCrossover performs a \n    one-point crossover at the byte level on a pair of parent individuals to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function ByteOnePointCrossover.__init__>, 'get_recombinations': <function ByteOnePointCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -294,17 +247,6 @@

    Submodules -
    -__module__ = 'src.recombination.byte_one_point_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_recombinations() → List[Individual][source]
    @@ -338,16 +280,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.byte_uniform_crossover', '__doc__': '\n    ByteUniformCrossover operator defined in (Satman, 2013). ByteUniformCrossover performs a \n    uniform crossover at the byte level on a pair of parent individuals to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function ByteUniformCrossover.__init__>, 'combine': <function ByteUniformCrossover.combine>, 'get_recombinations': <function ByteUniformCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -362,17 +294,6 @@

    Submodules -
    -__module__ = 'src.recombination.byte_uniform_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
    @@ -427,16 +348,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.flat_crossover', '__doc__': '\n    FlatCrossover performs a flat crossover on a pair of parent individuals\n    to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function FlatCrossover.__init__>, 'combine': <function FlatCrossover.combine>, 'get_recombinations': <function FlatCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -451,17 +362,6 @@

    Submodules -
    -__module__ = 'src.recombination.flat_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
    @@ -516,16 +416,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.linear_crossover', '__doc__': '\n    LinearCrossover performs a linear crossover on a pair of parent individuals\n    to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function LinearCrossover.__init__>, 'combine': <function LinearCrossover.combine>, 'get_recombinations': <function LinearCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -540,17 +430,6 @@

    Submodules -
    -__module__ = 'src.recombination.linear_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
    @@ -605,16 +484,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.one_point_crossover', '__doc__': '\n    OnePointCrossover performs a one-point crossover on a pair of parent individuals\n    to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function OnePointCrossover.__init__>, 'get_recombinations': <function OnePointCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -629,17 +498,6 @@

    Submodules -
    -__module__ = 'src.recombination.one_point_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_recombinations() → List[Individual][source]
    @@ -673,16 +531,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.pmx_crossover', '__doc__': '\n    PMXCrossover performs Partially Mapped Crossover (PMX) on a pair of parent individuals\n    to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function PMXCrossover.__init__>, 'get_recombinations': <function PMXCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -697,17 +545,6 @@

    Submodules -
    -__module__ = 'src.recombination.pmx_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_recombinations() → List[Individual][source]
    @@ -731,22 +568,6 @@

    Submodules class src.recombination.recombination_operator.RecombinationOperator[source]

    Bases: object

    -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.recombination_operator', 'get_recombinations': <function RecombinationOperator.get_recombinations>, '__dict__': <attribute '__dict__' of 'RecombinationOperator' objects>, '__weakref__': <attribute '__weakref__' of 'RecombinationOperator' objects>, '__doc__': None, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.recombination.recombination_operator'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_recombinations() → list[source]
    @@ -771,16 +592,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.two_point_crossover', '__doc__': '\n    TwoPointCrossover performs a two-point crossover on a pair of parent individuals\n    to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function TwoPointCrossover.__init__>, 'get_recombinations': <function TwoPointCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -795,17 +606,6 @@

    Submodules -
    -__module__ = 'src.recombination.two_point_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_recombinations() → List[Individual][source]
    @@ -839,16 +639,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.unfair_avarage_crossover', '__doc__': '\n    UnfairAvarageCrossover performs an unfair average crossover on a pair of parent individuals\n    to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function UnfairAvarageCrossover.__init__>, 'get_recombinations': <function UnfairAvarageCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -863,17 +653,6 @@

    Submodules -
    -__module__ = 'src.recombination.unfair_avarage_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_recombinations() → List[Individual][source]
    @@ -907,16 +686,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.recombination.uniform_crossover', '__doc__': '\n    UniformCrossover performs a uniform crossover on a pair of parent individuals\n    to produce offspring individuals.\n\n    Parameters\n    ----------\n    parents : list\n        A list containing two parent individuals.\n    problem : AbstractProblem\n        The problem instance that provides the fitness function.\n    ', '__init__': <function UniformCrossover.__init__>, 'combine': <function UniformCrossover.combine>, 'get_recombinations': <function UniformCrossover.get_recombinations>, '__annotations__': {}})
    -
    -
    __init__(parents: list, problem: AbstractProblem)[source]
    @@ -931,17 +700,6 @@

    Submodules -
    -__module__ = 'src.recombination.uniform_crossover'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
    diff --git a/src.selection.html b/src.selection.html index 338c2a8..a8f9429 100644 --- a/src.selection.html +++ b/src.selection.html @@ -118,11 +118,6 @@

    Submodules -
    -__dict__ = mappingproxy({'__module__': 'src.selection.roulette_wheel_selection', '__doc__': '\n    RouletteWheelSelection performs a roulette wheel selection on a population of individuals\n    to select parent individuals for crossover.\n\n    Parameters\n    ----------\n    pop_list : list of Individual\n        The population of individuals to select from.\n    c : int\n        The index of the individual to start selection from.\n    ', '__init__': <function RouletteWheelSelection.__init__>, 'get_parents': <function RouletteWheelSelection.get_parents>, '__annotations__': {}})
    -

    -
    __init__(pop_list: List[Individual] = [], c: int = 0)[source]
    @@ -137,17 +132,6 @@

    Submodules -
    -__module__ = 'src.selection.roulette_wheel_selection'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_parents() → List[Individual][source]
    @@ -171,22 +155,6 @@

    Submodules class src.selection.selection_operator.SelectionOperator[source]

    Bases: object

    -
    -
    -__dict__ = mappingproxy({'__module__': 'src.selection.selection_operator', 'get_parents': <function SelectionOperator.get_parents>, '__dict__': <attribute '__dict__' of 'SelectionOperator' objects>, '__weakref__': <attribute '__weakref__' of 'SelectionOperator' objects>, '__doc__': None, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.selection.selection_operator'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_parents() → list[source]
    @@ -212,16 +180,6 @@

    Submodules -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.selection.tournament_selection', '__doc__': '\n    TournamentSelection performs a tournament selection on a population of individuals\n    to select parent individuals for crossover.\n\n    Parameters\n    ----------\n    pop_list : list of Individual\n        The population of individuals to select from.\n    c : int\n        The index of the individual to start selection from.\n    K : int\n        The number of individuals to be chosen at random from neighbors.\n    ', '__init__': <function TournamentSelection.__init__>, 'get_parents': <function TournamentSelection.get_parents>, '__annotations__': {}})
    -
    -
    __init__(pop_list: List[Individual] = [], c: int = 0, K: int = 2)[source]
    @@ -237,17 +195,6 @@

    Submodules -
    -__module__ = 'src.selection.tournament_selection'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    get_parents() → List[Individual][source]
    diff --git a/src.tests.html b/src.tests.html index 5e372b7..3b5589a 100644 --- a/src.tests.html +++ b/src.tests.html @@ -135,27 +135,6 @@

    Submodulesclass src.tests.test_arithmetic_crossover.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_arithmetic_crossover', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_arithmetic_crossover'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -285,27 +264,6 @@

    Submodulesclass src.tests.test_blxalpha_crossover.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_blxalpha_crossover', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_blxalpha_crossover'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -401,27 +359,6 @@

    Submodulesclass src.tests.test_byte_mutation.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_byte_mutation', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_byte_mutation'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -493,27 +430,6 @@

    Submodulesclass src.tests.test_byte_mutation_random.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_byte_mutation_random', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_byte_mutation_random'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -585,27 +501,6 @@

    Submodulesclass src.tests.test_byte_one_point_crossover.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_byte_one_point_crossover', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_byte_one_point_crossover'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -704,27 +599,6 @@

    Submodulesclass src.tests.test_byte_uniform_crossover.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_byte_uniform_crossover', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_byte_uniform_crossover'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -995,27 +869,6 @@

    Submodulesclass src.tests.test_flat_crossover.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_flat_crossover', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_flat_crossover'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -1087,27 +940,6 @@

    Submodulesclass src.tests.test_float_uniform_mutation.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_float_uniform_mutation', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_float_uniform_mutation'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -1460,27 +1292,6 @@

    Assertionsclass src.tests.test_linear_crossover.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_linear_crossover', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_linear_crossover'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -1681,27 +1492,11 @@

    Assertionsobject

    Example problem class to be maximized for binary chromosomes.

    This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.

    -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_alpha_cga', '__doc__': '\n    Example problem class to be maximized for binary chromosomes.\n\n    This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.\n    ', '__init__': <function BinaryProblem.__init__>, 'f': <function BinaryProblem.f>, '__dict__': <attribute '__dict__' of 'BinaryProblem' objects>, '__weakref__': <attribute '__weakref__' of 'BinaryProblem' objects>, '__annotations__': {}})
    -
    -
    __init__()[source]
    -
    -
    -__module__ = 'src.tests.test_optimizer_alpha_cga'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x)[source]
    @@ -1729,11 +1524,6 @@

    Assertions -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_alpha_cga', '__doc__': '\n    Example problem class to be minimized using a permutation-based approach.\n\n    This class implements a simple objective function that measures the sum of absolute differences\n    between the chromosome and a target permutation.\n    ', '__init__': <function PermutationProblem.__init__>, 'f': <function PermutationProblem.f>, 'test_optimizer_alpha_cga_permutation': <function PermutationProblem.test_optimizer_alpha_cga_permutation>, '__dict__': <attribute '__dict__' of 'PermutationProblem' objects>, '__weakref__': <attribute '__weakref__' of 'PermutationProblem' objects>, '__annotations__': {}})
    -

    -
    __init__(target: List[int])[source]
    @@ -1745,17 +1535,6 @@

    Assertions -
    -__module__ = 'src.tests.test_optimizer_alpha_cga'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: List[int]) → float[source]
    @@ -1789,27 +1568,11 @@

    Assertions -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_alpha_cga', '__doc__': '\n    Example problem class to be minimized.\n\n    This class implements a simple sum of squares function with a global minimum value of 0,\n    achieved when all elements of the chromosome are equal to 0.\n    ', '__init__': <function RealProblem.__init__>, 'f': <function RealProblem.f>, '__dict__': <attribute '__dict__' of 'RealProblem' objects>, '__weakref__': <attribute '__weakref__' of 'RealProblem' objects>, '__annotations__': {}})
    -

    -
    __init__()[source]
    -
    -
    -__module__ = 'src.tests.test_optimizer_alpha_cga'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x)[source]
    @@ -1857,27 +1620,11 @@

    Assertionsobject

    Example problem class to be maximized for binary chromosomes.

    This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.

    -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_ccga', '__doc__': '\n    Example problem class to be maximized for binary chromosomes.\n\n    This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.\n    ', '__init__': <function BinaryProblem.__init__>, 'f': <function BinaryProblem.f>, '__dict__': <attribute '__dict__' of 'BinaryProblem' objects>, '__weakref__': <attribute '__weakref__' of 'BinaryProblem' objects>, '__annotations__': {}})
    -
    -
    __init__()[source]
    -
    -
    -__module__ = 'src.tests.test_optimizer_ccga'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x)[source]
    @@ -1913,27 +1660,11 @@

    Assertionsobject

    Example problem class to be maximized for binary chromosomes.

    This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.

    -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_cga', '__doc__': '\n    Example problem class to be maximized for binary chromosomes.\n\n    This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.\n    ', '__init__': <function BinaryProblem.__init__>, 'f': <function BinaryProblem.f>, '__dict__': <attribute '__dict__' of 'BinaryProblem' objects>, '__weakref__': <attribute '__weakref__' of 'BinaryProblem' objects>, '__annotations__': {}})
    -
    -
    __init__()[source]
    -
    -
    -__module__ = 'src.tests.test_optimizer_cga'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x)[source]
    @@ -1961,11 +1692,6 @@

    Assertions -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_cga', '__doc__': '\n    Example problem class to be minimized using a permutation-based approach.\n\n    This class implements a simple objective function that measures the sum of absolute differences\n    between the chromosome and a target permutation.\n    ', '__init__': <function PermutationProblem.__init__>, 'f': <function PermutationProblem.f>, 'test_optimizer_cga_permutation': <function PermutationProblem.test_optimizer_cga_permutation>, '__dict__': <attribute '__dict__' of 'PermutationProblem' objects>, '__weakref__': <attribute '__weakref__' of 'PermutationProblem' objects>, '__annotations__': {}})
    -

    -
    __init__(target: List[int])[source]
    @@ -1977,17 +1703,6 @@

    Assertions -
    -__module__ = 'src.tests.test_optimizer_cga'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: List[int]) → float[source]
    @@ -2021,27 +1736,11 @@

    Assertions -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_cga', '__doc__': '\n    Example problem class to be minimized.\n\n    This class implements a simple sum of squares function with a global minimum value of 0,\n    achieved when all elements of the chromosome are equal to 0.\n    ', '__init__': <function RealProblem.__init__>, 'f': <function RealProblem.f>, '__dict__': <attribute '__dict__' of 'RealProblem' objects>, '__weakref__': <attribute '__weakref__' of 'RealProblem' objects>, '__annotations__': {}})
    -

    -
    __init__()[source]
    -
    -
    -__module__ = 'src.tests.test_optimizer_cga'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x)[source]
    @@ -2090,27 +1789,11 @@

    Assertions -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_mccga', '__doc__': '\n    Example problem class to be minimized.\n\n    This class implements a simple sum of squares function with a global minimum value of 0,\n    achieved when all elements of the chromosome are equal to 0.\n    ', '__init__': <function RealProblem.__init__>, 'f': <function RealProblem.f>, '__dict__': <attribute '__dict__' of 'RealProblem' objects>, '__weakref__': <attribute '__weakref__' of 'RealProblem' objects>, '__annotations__': {}})
    -

    -
    __init__()[source]
    -
    -
    -__module__ = 'src.tests.test_optimizer_mccga'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x)[source]
    @@ -2146,27 +1829,11 @@

    Assertionsobject

    Example problem class to be maximized for binary chromosomes.

    This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.

    -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_sync_cga', '__doc__': '\n    Example problem class to be maximized for binary chromosomes.\n\n    This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.\n    ', '__init__': <function BinaryProblem.__init__>, 'f': <function BinaryProblem.f>, '__dict__': <attribute '__dict__' of 'BinaryProblem' objects>, '__weakref__': <attribute '__weakref__' of 'BinaryProblem' objects>, '__annotations__': {}})
    -
    -
    __init__()[source]
    -
    -
    -__module__ = 'src.tests.test_optimizer_sync_cga'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x)[source]
    @@ -2194,11 +1861,6 @@

    Assertions -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_sync_cga', '__doc__': '\n    Example problem class to be minimized using a permutation-based approach.\n\n    This class implements a simple objective function that measures the sum of absolute differences\n    between the chromosome and a target permutation.\n    ', '__init__': <function PermutationProblem.__init__>, 'f': <function PermutationProblem.f>, 'test_optimizer_sync_cga_permutation': <function PermutationProblem.test_optimizer_sync_cga_permutation>, '__dict__': <attribute '__dict__' of 'PermutationProblem' objects>, '__weakref__': <attribute '__weakref__' of 'PermutationProblem' objects>, '__annotations__': {}})
    -

    -
    __init__(target: List[int])[source]
    @@ -2210,17 +1872,6 @@

    Assertions -
    -__module__ = 'src.tests.test_optimizer_sync_cga'
    -

    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: List[int]) → float[source]
    @@ -2254,27 +1905,11 @@

    Assertions -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_optimizer_sync_cga', '__doc__': '\n    Example problem class to be minimized.\n\n    This class implements a simple sum of squares function with a global minimum value of 0,\n    achieved when all elements of the chromosome are equal to 0.\n    ', '__init__': <function RealProblem.__init__>, 'f': <function RealProblem.f>, '__dict__': <attribute '__dict__' of 'RealProblem' objects>, '__weakref__': <attribute '__weakref__' of 'RealProblem' objects>, '__annotations__': {}})
    -

    -
    __init__()[source]
    -
    -
    -__module__ = 'src.tests.test_optimizer_sync_cga'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x)[source]
    @@ -2364,27 +1999,6 @@

    Assertions -
    -__annotations__ = {}
    -

    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_population', '__doc__': '\n    A mock problem for testing purposes.\n\n    Methods\n    -------\n    f(chromosome : List[float]) -> float\n        Returns the sum of the chromosome as the fitness value.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_population'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(chromosome: List[float]) → float[source]
    @@ -2493,27 +2107,6 @@

    AssertionsNotes

    -5.0 ≤ xi ≤ 15.0 Global minimum at f(5, 7, 9, 3, 2) = 0

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_pow_function', '__doc__': '\n    Pow function implementation for optimization problems.\n\n    The Pow function is widely used for testing optimization algorithms.\n    The function is usually evaluated on the hypercube x_i ∈ [-5.0, 15.0].\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    f(x: list) -> float\n        Calculates the Pow function value for a given list of variables.\n\n    Notes\n    -----\n    -5.0 ≤ xi ≤ 15.0\n    Global minimum at f(5, 7, 9, 3, 2) = 0\n    ', 'f': <function Pow.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_pow_function'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]
    @@ -2951,27 +2544,6 @@

    Assertionsclass src.tests.test_unfair_average_crossover.MockProblem[source]

    Bases: AbstractProblem

    A mock problem class for testing purposes.

    -
    -
    -__annotations__ = {}
    -
    - -
    -
    -__dict__ = mappingproxy({'__module__': 'src.tests.test_unfair_average_crossover', '__doc__': '\n    A mock problem class for testing purposes.\n    ', 'f': <function MockProblem.f>, '__annotations__': {}})
    -
    - -
    -
    -__module__ = 'src.tests.test_unfair_average_crossover'
    -
    - -
    -
    -__weakref__
    -

    list of weak references to the object

    -
    -
    f(x: list) → float[source]