import pygad
import numpy
num_generations = 50 # Number of generations.
sol_per_pop = 8 # Number of solutions in the population.
num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool.
# Parameters of the mutation operation.
mutation_percent_genes = 10 # 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.
parent_selection_type = "tournament" # Type of parent selection.
crossover_type = "two_points" # Type of the crossover operator.
mutation_type = "scramble" # Type of the mutation operator.
keep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.
init_range_low = -2
init_range_high = 5
"""
Given the following function:
y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6
where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7) and y=44
What are the best values for the 6 weights (w1 to w6)? We are going to use the genetic algorithm to optimize this function.
"""
function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
desired_output = 44 # Function output.
num_genes = len(function_inputs)
def fitness_func(solution):
# Calculating the fitness value of each solution in the current population.
# The fitness function calulates the sum of products between each input and its corresponding weight.
output = numpy.sum(solution*function_inputs)
fitness = 1.0 / numpy.abs(output - desired_output)
return fitness
# Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
ga_instance = pygad.GA(num_generations=num_generations,
sol_per_pop=sol_per_pop,
num_parents_mating=num_parents_mating,
num_genes=num_genes,
fitness_func=fitness_func,
mutation_percent_genes=mutation_percent_genes,
mutation_num_genes=mutation_num_genes,
init_range_low=init_range_low,
init_range_high=init_range_high,
parent_selection_type=parent_selection_type,
crossover_type=crossover_type,
mutation_type=mutation_type,
keep_parents=keep_parents,
K_tournament=3)
# Running the GA to optimize the parameters of the function.
ga_instance.run()
# After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.
ga_instance.plot_result()
# Returning the details of the best solution.
best_solution, best_solution_fitness = ga_instance.best_solution()
print("Parameters of the best solution :", best_solution)
print("Fitness value of the best solution :", best_solution_fitness, "\n")
# Saving the GA instance.
filename = 'genetic' # The filename to which the instance is saved. The name is without extension.
ga_instance.save(filename=filename)
# Loading the saved GA instance.
loaded_ga_instance = pygad.load(filename=filename)
print("The saved instance of the genetic algorithm is loaded successfully.")
loaded_ga_instance.plot_result()
print(loaded_ga_instance.best_solution())