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Merge pull request #37 from jbytecode/main
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refactor code
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@jbytecode
jbytecode authored Aug 9, 2024
2 parents 71e4d96 + 802342a commit 32bd5fa
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Showing 5 changed files with 82 additions and 155 deletions.
114 changes: 25 additions & 89 deletions src/individual.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
from numpy import random
import numpy as np
import random as rd
from problems.abstract_problem import AbstractProblem

Expand All @@ -21,24 +22,30 @@ class Individual:
neighbors : list or None
The list of neighbors for the individual.
gen_type : str
The type of genome representation ("Binary", "Permutation", "Real-valued").
The type of genome representation ("Binary", "Permutation", "Real").
ch_size : int
The size of the chromosome.
"""

def __init__(self,
gen_type: str = "",
ch_size: int = 0,
problem: AbstractProblem = None):
problem: AbstractProblem = None,
mins : list[float] = [],
maxs : list[float] = []):
"""
Initialize an Individual with a specific genome type and chromosome size.
Parameters
----------
gen_type : str, optional
The type of genome representation. Must be one of "Binary", "Permutation", or "Real-valued". (default is "Binary")
The type of genome representation. Must be one of "Binary", "Permutation", or "Real". (default is "Binary")
ch_size : int
The size of the chromosome.
mins: list[float]
The minimum values for each gene in the chromosome.
maxs: list[float]
The maximum values for each gene in the chromosome.
"""
self.gen_type = gen_type
self.ch_size = ch_size
Expand All @@ -48,6 +55,8 @@ def __init__(self,
self.position = (0, 0)
self.neighbors_positions = None
self.neighbors = None
self.mins = mins
self.maxs = maxs


def randomize(self):
Expand All @@ -67,93 +76,20 @@ def randomize(self):
problem_name = self.problem.__class__.__name__

if self.gen_type == "Binary":

# CountSat, Fms, Mmdp, OneMax, Ecc, Maxcut20_01, Maxcut20_09, Maxcut100
if problem_name in ["CountSat", "Fms", "Mmdp", "OneMax", "Ecc", "Maxcut20_01", "Maxcut20_09", "Maxcut100"]:
self.chromosome = [random.randint(2) for i in range(self.ch_size)]
# Peak
elif problem_name == "Peak":
self.chromosome = [[random.randint(2) for g in range(self.ch_size)] for h in range(self.ch_size)]

elif self.gen_type == "Permutation":

# Tsp
if problem_name == "Tsp":
self.chromosome = list(rd.sample(range(1, 15), self.ch_size))

elif self.gen_type == "Real-valued":

# Ackley
if problem_name == "Ackley":
self.chromosome = [round(rd.uniform(-32.768, 32.768), 3) for i in range(self.ch_size)]

# Bohachevsky
elif problem_name == "Bohachevsky":
self.chromosome = [random.randint(-15.0, 16.0)for i in range(self.ch_size)]

# Fms
elif problem_name == "Fms":
self.chromosome = [round(rd.uniform(-6.4, 6.35), 3) for i in range(self.ch_size)]

# Griewank
elif problem_name == "Griewank":
self.chromosome = [round(rd.uniform(-600, 600), 3) for i in range(self.ch_size)]

# Rastrigin
elif problem_name == "Rastrigin":
self.chromosome = [round(rd.uniform(-5.12, 5.13), 2) for i in range(self.ch_size)]

# Rosenbrock
elif problem_name == "Rosenbrock":
self.chromosome = [random.randint(-5.0, 11.0) for i in range(self.ch_size)]

# Schaffer and Schaffer2, Bentcigar, Rothellipsoid
elif problem_name in ["Schaffer", "Schaffer2", "Bentcigar", "Rothellipsoid"]:
self.chromosome = [round(rd.uniform(-100, 100), 3) for i in range(self.ch_size)]

# Matyas, Sumofdifferentpowers
elif problem_name in ["Matyas", "Sumofdifferentpowers", "Holzman"]:
self.chromosome = [round(rd.uniform(-10.0, 10.0), 3) for i in range(self.ch_size)]

# Powell
elif problem_name == "Powell":
self.chromosome = [round(rd.uniform(-4.0, 5.0), 3) for i in range(self.ch_size)]

# Chichinadze
elif problem_name == "Chichinadze":
self.chromosome = [round(rd.uniform(-30, 30), 5) for i in range(self.ch_size)]

# Levy
elif problem_name == "Levy":
self.chromosome = [round(rd.uniform(-10.0, 10.0), 2) for i in range(self.ch_size)]

# Zettle
elif problem_name == "Zettle":
self.chromosome = [round(rd.uniform(-5.0, 5.0), 4) for i in range(self.ch_size)]

# Dropwave, Sphere
elif problem_name in ["Dropwave", "Sphere"]:
self.chromosome = [round(rd.uniform(-5.12, 5.12), 3) for i in range(self.ch_size)]

# # StyblinskiTang
elif problem_name == "StyblinskiTang":
self.chromosome = [round(rd.uniform(-5.0, 5.0), 6) for i in range(self.ch_size)]
self.chromosome = [random.randint(2) for i in range(self.ch_size)]

# # Threehumps
elif problem_name == "Threehumps":
self.chromosome = [round(rd.uniform(-5.0, 5.0), 3) for i in range(self.ch_size)]

# # Zakharov
elif problem_name == "Zakharov":
self.chromosome = [round(rd.uniform(-5.0, 10.0), 3) for i in range(self.ch_size)]

# # Schwefel
elif problem_name == "Schwefel":
self.chromosome = [round(rd.uniform(-500.0, 500.0), 3) for i in range(self.ch_size)]

# # Pow
elif problem_name == "Pow":
self.chromosome = [round(rd.uniform(-5.0, 15.0), 2) for i in range(self.ch_size)]
elif self.gen_type == "Permutation":
# Generate a random permutation of the numbers 1 to ch_size.
# by default random.permutation emits numbers from 0 to ch_size-1
# so we add 1 to each number to get the desired range.
self.chromosome = list(np.random.permutation(self.ch_size) + 1)

elif self.gen_type == "Real":
if len(self.mins) > 0:
assert len(self.mins) == len(self.maxs) == self.ch_size
self.chromosome = [rd.uniform(self.mins[i], self.maxs[i]) for i in range(self.ch_size)]
else:
self.chromosome = [rd.uniform(-1.0, 0.1) for i in range(self.ch_size)]

else:
raise NotImplementedError(self.gen_type + " not implemented yet.")
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8 changes: 4 additions & 4 deletions src/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
n_rows = 5,
n_gen = 100,
ch_size = 5,
gen_type = "Real-valued",
gen_type = "Real",
p_crossover = 0.9,
p_mutation = 0.2,
problem = optimizer.Ackley(),
Expand Down Expand Up @@ -56,7 +56,7 @@
# n_rows = 5,
# n_gen = 100,
# ch_size = 5,
# gen_type = "Real-valued",
# gen_type = "Real",
# p_crossover = 0.9,
# p_mutation = 0.2,
# problem = optimizer.Ackley(),
Expand Down Expand Up @@ -105,7 +105,7 @@
# n_rows = 5,
# n_gen = 100,
# ch_size = 10,
# gen_type = "Real-valued",
# gen_type = "Real",
# p_crossover = 0.9,
# p_mutation = 0.2,
# problem = optimizer.Ackley(),
Expand Down Expand Up @@ -190,7 +190,7 @@
# n_rows = 5,
# n_gen = 100,
# ch_size = 5,
# gen_type = "Real-valued",
# gen_type = "Real",
# problem = optimizer.Ackley(),
# selection = optimizer.TournamentSelection,
# min_value = -32.768,
Expand Down
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