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74 lines (53 loc) · 2.57 KB
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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()