import pygad

import numpy

"""

This script gives an example of using custom user-defined functions for the 3 operators:

1) Parent selection.

2) Crossover.

3) Mutation.

For more information, check the User-Defined Crossover, Mutation, and Parent Selection Operators section in the documentation:

https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#user-defined-crossover-mutation-and-parent-selection-operators

"""

equation_inputs = [4,-2,3.5]

desired_output = 44

def fitness_func(solution, solution_idx):

output = numpy.sum(solution * equation_inputs)

fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)

return fitness

def parent_selection_func(fitness, num_parents, ga_instance):

# Selects the best {num_parents} parents. Works as steady-state selection.

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

fitness_sorted.reverse()

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

for parent_num in range(num_parents):

parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy()

return parents, fitness_sorted[:num_parents]

def crossover_func(parents, offspring_size, ga_instance):

# This is single-point crossover.

offspring = []

idx = 0

while len(offspring) != offspring_size[0]:

parent1 = parents[idx % parents.shape[0], :].copy()

parent2 = parents[(idx + 1) % parents.shape[0], :].copy()

random_split_point = numpy.random.choice(range(offspring_size[0]))

parent1[random_split_point:] = parent2[random_split_point:]

offspring.append(parent1)

idx += 1

return numpy.array(offspring)

def mutation_func(offspring, ga_instance):

# This is random mutation that mutates a single gene.

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

# Make some random changes in 1 or more genes.

random_gene_idx = numpy.random.choice(range(offspring.shape[0]))

offspring[chromosome_idx, random_gene_idx] += numpy.random.random()

return offspring

ga_instance = pygad.GA(num_generations=10,

sol_per_pop=5,

num_parents_mating=2,

num_genes=len(equation_inputs),

fitness_func=fitness_func,

parent_selection_type=parent_selection_func,

crossover_type=crossover_func,

mutation_type=mutation_func)

ga_instance.run()

ga_instance.plot_fitness()