# Parameters of the genetic algorithm.

sol_per_pop = None # Number of solutions in the population.

num_parents_mating = None # Number of solutions to be selected as parents in the mating pool.

num_generations = None # Number of generations.

pop_size = None # Population size = (number of chromosomes, number of genes per chromosome)

keep_parents = -1 # If 0, this means the parents of the current populaiton will not be used at all in the next population. If -1, this means all parents in the current population will be used in the next population. If set to a value > 0, then the specified value refers to the number of parents in the current population to be used in the next population. In some cases, the parents are of high quality and thus we do not want to loose such some high quality solutions. If some parent selection operators like roulette wheel selection (RWS), the parents may not be of high quality and thus keeping the parents might degarde the quality of the population.

fitness_func = None

# Parameters about parent selection.

parent_selection_type = None # Type of parent selection.

select_parents = None # Refers to a method that selects the parents based on the parent selection type specified in parent_selection_type.

K_tournament = None # For tournament selection, a parent is selected out of K randomly selected solutions.

population = None # A NumPy array holding the opulation.

crossover_type = None # Type of the crossover opreator.

crossover = None # A method that applies the crossover operator based on the selected type of crossover in the crossover_type property.

mutation_type = None # Type of the mutation opreator.

mutation = None # A method that applies the mutation operator based on the selected type of mutation in the mutation_type property.

best_solution_fitness = [] # A list holding the fitness value of the best solution for each generation.

# Parameters of the function to be optimized.

function_inputs = None # Inputs of the function to be optimized.

function_output = None # Desired outuput of the function.

num_genes = None

# Mutation parameters.

mutation_percent_genes=None # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists.

mutation_num_genes=None # Number of genes to mutate. If the parameter mutation_num_genes exists, then no need for the parameter mutation_percent_genes.

random_mutation_min_val=None

random_mutation_max_val=None

# Some flags.

run_completed = False # Set to True only when the run() method completes gracefully.

valid_parameters = False # Set to True when all the paremeters passed in the GA class construtor are valid.

def __init__(self, num_generations,

sol_per_pop,

num_parents_mating,

num_genes,

fitness_func,

parent_selection_type="sss",

keep_parents=-1,

K_tournament=3,

crossover_type="single_point",

mutation_type="random",

mutation_percent_genes=10,

mutation_num_genes=None,

random_mutation_min_val=-1.0,

random_mutation_max_val=1.0):

# Validating the number of solutions in the population (sol_per_pop) and the number of parents to be selected for mating (num_parents_mating)

if (sol_per_pop <= 0 or num_parents_mating <= 0):

print("ERROR creating an instance of the GA class with invalid parameters. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n")

self.valid_parameters = False

return

# Validating the number of gene.

if (num_genes <= 0):

print("ERROR: Number of genes cannot be <= 0. \n")

self.valid_parameters = False

return

self.num_genes = num_genes # Number of genes in the solution.

if (mutation_num_genes == None):

if (mutation_percent_genes < 0 or mutation_percent_genes > 100):

print("ERROR: Percentage of selected genes for mutation (mutation_percent_genes) must be >= 0 and <= 100 inclusive.\n")

self.valid_parameters = False

return

elif (mutation_num_genes <= 0 ):

print("ERROR: Number of selected genes for mutation (mutation_num_genes) cannot be <= 0.\n")

self.valid_parameters = False

return

elif (mutation_num_genes > self.num_genes):

print("ERROR: Number of selected genes for mutation (mutation_num_genes) cannot be greater than the number of parameters in the function.\n")

self.valid_parameters = False

return

elif (type(mutation_num_genes) is not int):

print("Warning: Number of selected genes for mutation (mutation_num_genes) must be a positive integer >= 1.\n")

self.valid_parameters = False

return

# Validating the number of parents to be selected for mating: num_parents_mating

if (num_parents_mating > sol_per_pop):

print("ERROR creating an instance of the GA class with invalid parameters. \nThe number of parents to select for mating cannot be greater than the number of solutions in the population (i.e., num_parents_mating must always be <= sol_per_pop).\n")

self.valid_parameters = False

return

# Validating the crossover type: crossover_type

if (crossover_type == "single_point"):

self.crossover = self.single_point_crossover

elif (crossover_type == "two_points"):

self.crossover = self.two_points_crossover

elif (crossover_type == "uniform"):

self.crossover = self.uniform_crossover

else:

print("ERROR: undefined crossover type. \nThe assigned value to the 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")

self.valid_parameters = False

return

self.crossover_type = crossover_type

# Validating the mutation type: mutation_type

if (mutation_type == "random"):

self.mutation = self.random_mutation

elif (mutation_type == "swap"):

self.mutation = self.swap_mutation

elif (mutation_type == "scramble"):

self.mutation = self.scramble_mutation

elif (mutation_type == "inversion"):

self.mutation = self.inversion_mutation

else:

print("ERROR: undefined mutation type. \nThe assigned value to the mutation_type argument does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation).\n")

self.valid_parameters = False

return

self.mutation_type = mutation_type

# Validating the selected type of parent selection: parent_selection_type

if (parent_selection_type == "sss"):

self.select_parents = self.steady_state_selection

elif (parent_selection_type == "rws"):

self.select_parents = self.roulette_wheel_selection

elif (parent_selection_type == "sus"):

self.select_parents = self.stochastic_universal_selection

elif (parent_selection_type == "random"):

self.select_parents = self.random_selection

elif (parent_selection_type == "tournament"):

self.select_parents = self.tournament_selection

elif (parent_selection_type == "rank"):

self.select_parents = self.rank_selection

else:

print("ERROR: undefined parent selection type. \nThe assigned value to the parent_selection_type argument does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n")

self.valid_parameters = False

return

if(parent_selection_type == "tournament"):

if (K_tournament > sol_per_pop):

K_tournament = sol_per_pop

print("Warining: K of the tournament selection should not be greater than the number of solutions within the population.\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n")

elif (K_tournament <= 0):

print("ERROR: K of the tournament selection cannot be <=0.\n")

self.valid_parameters = False

return

self.K_tournament = K_tournament

# Validating the number of parents to keep in the next population: keep_parents

if (keep_parents > sol_per_pop or keep_parents > num_parents_mating or keep_parents < -1):

print("ERROR: Incorrect value to the keep_parents parameter. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.\n")

self.valid_parameters = False

return

self.keep_parents = keep_parents

if (self.keep_parents == -1): # Keep all parents in the next population.

self.num_offspring = sol_per_pop - num_parents_mating

elif (self.keep_parents == 0): # Keep no parents in the next population.

self.num_offspring = sol_per_pop

elif (self.keep_parents > 0): # Keep the specified number of parents in the next population.

self.num_offspring = sol_per_pop - self.keep_parents

# At this point, all necessary parameters validation is done successfully and we are sure that the parameters are valid.

self.valid_parameters = True

self.fitness_func = fitness_func

# Parameters of the genetic algorithm.

self.sol_per_pop = sol_per_pop

self.num_parents_mating = num_parents_mating

self.num_generations = abs(num_generations)

self.parent_selection_type = parent_selection_type

# Parameters of the mutation operation.

self.mutation_percent_genes = mutation_percent_genes

self.mutation_num_genes = mutation_num_genes

self.random_mutation_min_val = random_mutation_min_val

self.random_mutation_max_val = random_mutation_max_val

# Even such this parameter is declared in the class header, it is assigned to the object here to access it after saving the object.

self.best_solution_fitness = []

# Initializing the population.

self.initialize_population()

def initialize_population(self):

"""

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=-4.0, high=4.0, size=self.pop_size)

def run(self):

"""

if self.valid_parameters == False:

print("ERROR calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n")

return

for generation in range(self.num_generations):

# Measuring the fitness of each chromosome in the population.

fitness = self.cal_pop_fitness()

# Selecting the best parents in the population for mating.

parents = self.select_parents(fitness, num_parents=self.num_parents_mating)

# Generating next generation using crossover.

offspring_crossover = self.crossover(parents,

offspring_size=(self.num_offspring, self.num_genes))

# Adding some variations to the offspring using mutation.

offspring_mutation = self.mutation(offspring_crossover)

if (self.keep_parents == 0):

self.population = offspring_mutation

elif (self.keep_parents == -1):

# Creating the new population based on the parents and offspring.

self.population[0:parents.shape[0], :] = parents

self.population[parents.shape[0]:, :] = offspring_mutation

elif (self.keep_parents > 0):

parents_to_keep = self.steady_state_selection(fitness, num_parents=self.keep_parents)

self.population[0:parents_to_keep.shape[0], :] = parents_to_keep

self.population[parents_to_keep.shape[0]:, :] = offspring_mutation

# After the run() method completes, the run_completed flag is changed from False to True.

self.run_completed = True

def cal_pop_fitness(self):

"""

if self.valid_parameters == False:

print("ERROR calling the cal_pop_fitness() method: \nPlease check the parameters passed while creating an instance of the GA class.\n")

return []

pop_fitness = []

# Calculating the fitness value of each solution in the current population.

for sol in self.population:

fitness = self.fitness_func(sol)

pop_fitness.append(fitness)

pop_fitness = numpy.array(pop_fitness)

# The best result in the current iteration.

self.best_solution_fitness.append(numpy.max(pop_fitness))

return pop_fitness

def steady_state_selection(self, fitness, num_parents):

"""

fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k])

fitness_sorted.reverse()

# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.

parents = numpy.empty((num_parents, self.population.shape[1]))

for parent_num in range(num_parents):

parents[parent_num, :] = self.population[fitness_sorted[parent_num], :]

return parents

def rank_selection(self, fitness, num_parents):

"""

fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k])

fitness_sorted.reverse()

# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.

parents = numpy.empty((num_parents, self.population.shape[1]))

for parent_num in range(num_parents):

parents[parent_num, :] = self.population[fitness_sorted[parent_num], :]

return parents

def random_selection(self, fitness, num_parents):

"""

parents = numpy.empty((num_parents, self.population.shape[1]))

rand_indices = numpy.random.randint(low=0.0, high=fitness.shape[0], size=num_parents)

for parent_num in range(num_parents):

parents[parent_num, :] = self.population[rand_indices[parent_num], :]

return parents

def tournament_selection(self, fitness, num_parents):

"""

parents = numpy.empty((num_parents, self.population.shape[1]))

for parent_num in range(num_parents):

rand_indices = numpy.random.randint(low=0.0, high=len(fitness), size=self.K_tournament)

K_fitnesses = fitness[rand_indices]

selected_parent_idx = numpy.where(K_fitnesses == numpy.max(K_fitnesses))[0][0]

parents[parent_num, :] = self.population[rand_indices[selected_parent_idx], :]

return parents

def roulette_wheel_selection(self, fitness, num_parents):

"""

fitness_sum = numpy.sum(fitness)

probs = fitness / fitness_sum

probs_start = numpy.zeros(probs.shape, dtype=numpy.float) # An array holding the start values of the ranges of probabilities.

probs_end = numpy.zeros(probs.shape, dtype=numpy.float) # An array holding the end values of the ranges of probabilities.

curr = 0.0

# Calculating the probabilities of the solutions to form a roulette wheel.

for _ in range(probs.shape[0]):

min_probs_idx = numpy.where(probs == numpy.min(probs))[0][0]

probs_start[min_probs_idx] = curr

curr = curr + probs[min_probs_idx]

probs_end[min_probs_idx] = curr

probs[min_probs_idx] = 99999999999

# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.

parents = numpy.empty((num_parents, self.population.shape[1]))

for parent_num in range(num_parents):

rand_prob = numpy.random.rand()

for idx in range(probs.shape[0]):

if (rand_prob >= probs_start[idx] and rand_prob < probs_end[idx]):

parents[parent_num, :] = self.population[idx, :]

break

return parents

def stochastic_universal_selection(self, fitness, num_parents):

"""

# https://en.wikipedia.org/wiki/Stochastic_universal_sampling

# https://books.google.com.eg/books?id=gwUwIEPqk30C&pg=PA60&lpg=PA60&dq=Roulette+Wheel+genetic+algorithm+select+more+than+once&source=bl&ots=GLr2DrPcj4&sig=ACfU3U0jVOGXhzsla8mVqhi5x1giPRL4ew&hl=en&sa=X&ved=2ahUKEwim25rMvdzoAhWa8uAKHbt0AdgQ6AEwA3oECAYQLQ#v=onepage&q=Roulette%20Wheel%20&f=false

# https://www.tutorialspoint.com/genetic_algorithms/genetic_algorithms_parent_selection.htm

# https://www.obitko.com/tutorials/genetic-algorithms/selection.php

fitness_sum = numpy.sum(fitness)

probs = fitness / fitness_sum

probs_start = numpy.zeros(probs.shape, dtype=numpy.float) # An array holding the start values of the ranges of probabilities.

probs_end = numpy.zeros(probs.shape, dtype=numpy.float) # An array holding the end values of the ranges of probabilities.

curr = 0.0

# Calculating the probabilities of the solutions to form a roulette wheel.

for _ in range(probs.shape[0]):

min_probs_idx = numpy.where(probs == numpy.min(probs))[0][0]

probs_start[min_probs_idx] = curr

curr = curr + probs[min_probs_idx]

probs_end[min_probs_idx] = curr

probs[min_probs_idx] = 99999999999

pointers_distance = 1.0 / self.num_parents_mating # Distance between different pointers.

first_pointer = numpy.random.uniform(low=0.0, high=pointers_distance, size=1) # Location of the first pointer.

# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.

parents = numpy.empty((num_parents, self.population.shape[1]))

for parent_num in range(num_parents):

rand_pointer = first_pointer + parent_num*pointers_distance

for idx in range(probs.shape[0]):

if (rand_pointer >= probs_start[idx] and rand_pointer < probs_end[idx]):

parents[parent_num, :] = self.population[idx, :]

break

return parents

def single_point_crossover(self, parents, offspring_size):

"""

offspring = numpy.empty(offspring_size)

# The point at which crossover takes place between two parents. Usually, it is at the center.

crossover_point = numpy.random.randint(low=0, high=parents.shape[1], size=1)[0]

for k in range(offspring_size[0]):

# Index of the first parent to mate.

parent1_idx = k % parents.shape[0]

# Index of the second parent to mate.

parent2_idx = (k+1) % parents.shape[0]

# The new offspring will have its first half of its genes taken from the first parent.

offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]

# The new offspring will have its second half of its genes taken from the second parent.

offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]

return offspring

def two_points_crossover(self, parents, offspring_size):

"""

offspring = numpy.empty(offspring_size)

if (parents.shape[1] == 1): # If the chromosome has only a single gene. In this case, this gene is copied from the second parent.

crossover_point1 = 0

else:

crossover_point1 = numpy.random.randint(low=0, high=numpy.ceil(parents.shape[1]/2 + 1), size=1)[0]

crossover_point2 = crossover_point1 + int(parents.shape[1]/2) # The second point must always be greater than the first point.

for k in range(offspring_size[0]):

# Index of the first parent to mate.

parent1_idx = k % parents.shape[0]

# Index of the second parent to mate.

parent2_idx = (k+1) % parents.shape[0]

# The genes from the beginning of the chromosome up to the first point are copied from the first parent.

offspring[k, 0:crossover_point1] = parents[parent1_idx, 0:crossover_point1]

# The genes from the second point up to the end of the chromosome are copied from the first parent.

offspring[k, crossover_point2:] = parents[parent1_idx, crossover_point2:]

# The genes between the 2 points are copied from the second parent.

offspring[k, crossover_point1:crossover_point2] = parents[parent2_idx, crossover_point1:crossover_point2]

return offspring

def uniform_crossover(self, parents, offspring_size):

"""

offspring = numpy.empty(offspring_size)

for k in range(offspring_size[0]):

# Index of the first parent to mate.

parent1_idx = k % parents.shape[0]

# Index of the second parent to mate.

parent2_idx = (k+1) % parents.shape[0]

genes_source = numpy.random.randint(low=0, high=2, size=offspring_size[1])

for gene_idx in range(offspring_size[1]):

if (genes_source[gene_idx] == 0):

# The gene will be copied from the first parent if the current gene index is 0.

offspring[k, gene_idx] = parents[parent1_idx, gene_idx]

elif (genes_source[gene_idx] == 1):

# The gene will be copied from the second parent if the current gene index is 1.

offspring[k, gene_idx] = parents[parent2_idx, gene_idx]

return offspring

def random_mutation(self, 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 idx in range(offspring.shape[0]):

# The random value to be added to the gene.

random_value = numpy.random.uniform(self.random_mutation_min_val, self.random_mutation_max_val, 1)

offspring[idx, mutation_indices] = offspring[idx, mutation_indices] + random_value

return offspring

def swap_mutation(self, offspring):

"""

for idx in range(offspring.shape[0]):

mutation_gene1 = numpy.random.randint(low=0, high=offspring.shape[1]/2, size=1)[0]

mutation_gene2 = mutation_gene1 + int(offspring.shape[1]/2)

temp = offspring[idx, mutation_gene1]

offspring[idx, mutation_gene1] = offspring[idx, mutation_gene2]

offspring[idx, mutation_gene2] = temp

return offspring

def inversion_mutation(self, offspring):

"""

for idx in range(offspring.shape[0]):

mutation_gene1 = numpy.random.randint(low=0, high=numpy.ceil(offspring.shape[1]/2 + 1), size=1)[0]

mutation_gene2 = mutation_gene1 + int(offspring.shape[1]/2)

genes_to_scramble = numpy.flip(offspring[idx, mutation_gene1:mutation_gene2])

offspring[idx, mutation_gene1:mutation_gene2] = genes_to_scramble

return offspring

def scramble_mutation(self, offspring):

"""

for idx in range(offspring.shape[0]):

mutation_gene1 = numpy.random.randint(low=0, high=numpy.ceil(offspring.shape[1]/2 + 1), size=1)[0]

mutation_gene2 = mutation_gene1 + int(offspring.shape[1]/2)

genes_range = numpy.arange(start=mutation_gene1, stop=mutation_gene2)

numpy.random.shuffle(genes_range)

genes_to_scramble = numpy.flip(offspring[idx, genes_range])

offspring[idx, genes_range] = genes_to_scramble

return offspring

def best_solution(self):

"""

if self.run_completed == False:

print("Warning calling the best_solution() method: \nThe run() method is not yet called and thus the GA did not evolve the solutions. Thus, the best solution is retireved from the initial random population without being evolved.\n")

return [], []

# Getting the best solution after finishing all generations.

# At first, the fitness is calculated for each solution in the final generation.

fitness = self.cal_pop_fitness()

# Then return the index of that solution corresponding to the best fitness.

best_match_idx = numpy.where(fitness == numpy.max(fitness))

best_solution = self.population[best_match_idx, :][0][0]

best_solution_fitness = fitness[best_match_idx][0]

return best_solution, best_solution_fitness

def plot_result(self):

"""

if self.run_completed == False:

print("Warning calling the plot_result() method: \nGA is not executed yet and there are no results to display. Please call the run() method before calling the plot_result() method.\n")

matplotlib.pyplot.figure()

matplotlib.pyplot.plot(self.best_solution_fitness)

matplotlib.pyplot.xlabel("Iteration")

matplotlib.pyplot.ylabel("Fitness")

matplotlib.pyplot.show()

def save(self, filename):

"""

with open(filename + ".pkl", 'wb') as file:

"""

try:

with open(filename + ".pkl", 'rb') as file:

ga_in = pickle.load(file)

except (FileNotFoundError):

print("Error loading the file. Please check if the file exists.")