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Commit c4564ec

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PyGAD 2.16.0
A user-defined function can be passed to the mutation_type, crossover_type, and parent_selection_type parameters in the pygad.GA class to create a custom mutation, crossover, and parent selection operators. 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 The example_custom_operators.py script gives an example of building and using custom functions for the 3 operators. #50 
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‎__init__.py

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from .pygad import * # Relative import.
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__version__ = "2.15.1"
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__version__ = "2.16.0"

‎example_custom_operators.py

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import pygad
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import numpy
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"""
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This script gives an example of using custom user-defined functions for the 3 operators:
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1) Parent selection.
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2) Crossover.
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3) Mutation.
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For more information, check the User-Defined Crossover, Mutation, and Parent Selection Operators section in the documentation:
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https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#user-defined-crossover-mutation-and-parent-selection-operators
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"""
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equation_inputs = [4,-2,3.5]
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desired_output = 44
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def fitness_func(solution, solution_idx):
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output = numpy.sum(solution * equation_inputs)
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fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
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return fitness
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def parent_selection_func(fitness, num_parents, ga_instance):
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# Selects the best {num_parents} parents. Works as steady-state selection.
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fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k])
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fitness_sorted.reverse()
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parents = numpy.empty((num_parents, ga_instance.population.shape[1]))
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for parent_num in range(num_parents):
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parents[parent_num, :] = ga_instance.population[fitness_sorted[parent_num], :].copy()
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return parents, fitness_sorted[:num_parents]
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def crossover_func(parents, offspring_size, ga_instance):
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# This is single-point crossover.
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offspring = []
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idx = 0
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while len(offspring) != offspring_size[0]:
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parent1 = parents[idx % parents.shape[0], :].copy()
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parent2 = parents[(idx + 1) % parents.shape[0], :].copy()
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random_split_point = numpy.random.choice(range(offspring_size[0]))
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parent1[random_split_point:] = parent2[random_split_point:]
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offspring.append(parent1)
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idx += 1
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return numpy.array(offspring)
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def mutation_func(offspring, ga_instance):
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# This is random mutation that mutates a single gene.
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for chromosome_idx in range(offspring.shape[0]):
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# Make some random changes in 1 or more genes.
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random_gene_idx = numpy.random.choice(range(offspring.shape[0]))
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offspring[chromosome_idx, random_gene_idx] += numpy.random.random()
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return offspring
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ga_instance = pygad.GA(num_generations=10,
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sol_per_pop=5,
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num_parents_mating=2,
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num_genes=len(equation_inputs),
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fitness_func=fitness_func,
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parent_selection_type=parent_selection_func,
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crossover_type=crossover_func,
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mutation_type=mutation_func)
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ga_instance.run()
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ga_instance.plot_fitness()

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