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Commitc4564ec

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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-operatorsThe example_custom_operators.py script gives an example of building and using custom functions for the 3 operators.ahmedfgad#50
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‎__init__.py

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from .pygadimport*# 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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importpygad
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importnumpy
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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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deffitness_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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returnfitness
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defparent_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=lambdak: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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forparent_numinrange(num_parents):
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parents[parent_num, :]=ga_instance.population[fitness_sorted[parent_num], :].copy()
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returnparents,fitness_sorted[:num_parents]
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defcrossover_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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whilelen(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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returnnumpy.array(offspring)
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defmutation_func(offspring,ga_instance):
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# This is random mutation that mutates a single gene.
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forchromosome_idxinrange(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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returnoffspring
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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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