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109 changes: 94 additions & 15 deletions pygad.py
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Original file line number Diff line number Diff line change
@@ -1,16 +1,18 @@
import numpy
import random
import matplotlib.pyplot
import pickle
import random
import time

import matplotlib.pyplot
import numpy


class GA:
def __init__(self,
num_generations,
num_parents_mating,
def __init__(self,
num_generations,
num_parents_mating,
fitness_func,
initial_population=None,
sol_per_pop=None,
sol_per_pop=None,
num_genes=None,
init_range_low=-4,
init_range_high=4,
Expand All @@ -25,7 +27,8 @@ def __init__(self,
random_mutation_min_val=-1.0,
random_mutation_max_val=1.0,
callback_generation=None,
delay_after_gen=0.0):
delay_after_gen=0.0,
genes_range_dict=None):

"""
The constructor of the GA class accepts all parameters required to create an instance of the GA class. It validates such parameters.
Expand All @@ -35,7 +38,10 @@ def __init__(self,

fitness_func: Accepts a function that must accept 2 parameters (a single solution and its index in the population) and return the fitness value of the solution. Available starting from PyGAD 1.0.17 until 1.0.20 with a single parameter representing the solution. Changed in PyGAD 2.0.0 and higher to include the second parameter representing the solution index.

initial_population: A user-defined initial population. It is useful when the user wants to start the generations with a custom initial population. It defaults to None which means no initial population is specified by the user. In this case, PyGAD creates an initial population using the 'sol_per_pop' and 'num_genes' parameters. An exception is raised if the 'initial_population' is None while any of the 2 parameters ('sol_per_pop' or 'num_genes') is also None.
initial_population: A user-defined initial population.
It is useful when the user wants to start the generations with a custom initial population.
It defaults to None which means no initial population is specified by the user.
In this case, PyGAD creates an initial population using the 'sol_per_pop' and 'num_genes' parameters. An exception is raised if the 'initial_population' is None while any of the 2 parameters ('sol_per_pop' or 'num_genes') is also None.
sol_per_pop: Number of solutions in the population.
num_genes: Number of parameters in the function.

Expand Down Expand Up @@ -64,6 +70,8 @@ def __init__(self,

self.init_range_low = init_range_low
self.init_range_high = init_range_high
self.genes_range_dict=genes_range_dict


if initial_population is None:
if (sol_per_pop is None) or (num_genes is None):
Expand All @@ -81,7 +89,10 @@ def __init__(self,
# Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible.
self.num_genes = num_genes # Number of genes in the solution.
self.sol_per_pop = sol_per_pop # Number of solutions in the population.
self.initialize_population(self.init_range_low, self.init_range_high)
if mutation_type == "random_dict":
self.initialize_population_dict()
else:
self.initialize_population(self.init_range_low, self.init_range_high)
else:
raise TypeError("The expected type of both the sol_per_pop and num_genes parameters is int but {sol_per_pop_type} and {num_genes_type} found.".format(sol_per_pop_type=type(sol_per_pop), num_genes_type=type(num_genes)))
elif numpy.array(initial_population).ndim != 2:
Expand Down Expand Up @@ -117,14 +128,18 @@ def __init__(self,
self.crossover = None
else:
self.valid_parameters = False
raise ValueError("Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) argument does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover).\n".format(crossover_type=crossover_type))
raise ValueError(
"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) argument does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover).\n".format(
crossover_type=crossover_type))

self.crossover_type = crossover_type

# mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property.
# Validating the mutation type: mutation_type
if (mutation_type == "random"):
self.mutation = self.random_mutation
elif (mutation_type == "random_dict"):
self.mutation = self.random_dict_mutation
elif (mutation_type == "swap"):
self.mutation = self.swap_mutation
elif (mutation_type == "scramble"):
Expand Down Expand Up @@ -282,13 +297,50 @@ def initialize_population(self, low, high):
# Population size = (number of chromosomes, number of genes per chromosome)
self.pop_size = (self.sol_per_pop,self.num_genes) # The population will have sol_per_pop chromosome where each chromosome has num_genes genes.
# Creating the initial population randomly.
self.population = numpy.random.uniform(low=low,
high=high,
self.population = numpy.random.uniform(low=low,
high=high,
size=self.pop_size) # A NumPy array holding the initial population.

print(self.population)
print(type(self.population))

# Keeping the initial population in the initial_population attribute.
self.initial_population = self.population.copy()

def initialize_population_dict(self):

"""
Creates an initial population randomly as a NumPy array. The array is saved in the instance attribute named 'population'.

low: The lower value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20 and higher.
high: The upper value of the random range from which the gene values in the initial population are selected. It defaults to -4. Available in PyGAD 1.0.20.

This method assigns the values of the following 3 instance attributes:
1. pop_size: Size of the population.
2. population: Initially, holds the initial population and later updated after each generation.
3. init_population: Keeping the initial population.
"""
# Population size = (number of chromosomes, number of genes per chromosome)
self.pop_size = (self.sol_per_pop,
self.num_genes) # The population will have sol_per_pop chromosome where each chromosome has num_genes genes.
# Creating the initial population randomly.
self.population=[]
for i in range(self.sol_per_pop) :
chromosome = []
for key in list(self.genes_range_dict.keys()):
random_value_dict = random.choice(self.genes_range_dict[key])
chromosome.append(random_value_dict)

self.population.append(chromosome)

self.population=numpy.array (self.population)
print(self.population)
print(type(self.population))

# Keeping the initial population in the initial_population attribute.
self.initial_population = self.population.copy()



def cal_pop_fitness(self):

"""
Expand Down Expand Up @@ -629,6 +681,33 @@ def random_mutation(self, offspring):
offspring[offspring_idx, gene_idx] = offspring[offspring_idx, gene_idx] + random_value
return offspring

def random_dict_mutation(self, offspring):
"""
Applies the random mutation which changes the values of a number of genes randomly by selecting a random value between random_mutation_min_val and random_mutation_max_val to be added to the selected genes.
It accepts a single parameter:
-offspring: The offspring to mutate.
It returns an array of the mutated offspring.
"""

if self.mutation_num_genes == None:
self.mutation_num_genes = numpy.uint32((self.mutation_percent_genes * offspring.shape[1]) / 100)
# Based on the percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1.
if self.mutation_num_genes == 0:
self.mutation_num_genes = 1
mutation_indices = numpy.array(random.sample(range(0, offspring.shape[1]), self.mutation_num_genes))
# Random mutation changes a single gene in each offspring randomly.
for offspring_idx in range(offspring.shape[0]):
for gene_idx in mutation_indices:
# select a random value from the dictionary.
gene_name=list(self.genes_range_dict.keys())[gene_idx]
random_value_dict = random.choice(self.genes_range_dict[gene_name])
self.mutation_by_replacement = True

# If the mutation_by_replacement attribute is True, then the random value replaces the current gene value.
if self.mutation_by_replacement:
offspring[offspring_idx, gene_idx] = random_value_dict
return offspring

def swap_mutation(self, offspring):

"""
Expand Down Expand Up @@ -755,4 +834,4 @@ def load(filename):
raise FileNotFoundError("Error reading the file {filename}. Please check your inputs.".format(filename=filename))
except:
raise BaseException("Error loading the file. Please check if the file exists.")
return ga_in
return ga_in