PyGAD 2.16.0

ahmedfgad · web-flow · commit c4564ec831d6 · 2021-06-19T22:16:58.000-04:00
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. ahmedfgad#50
diff --git a/__init__.py b/__init__.py
@@ -1,3 +1,3 @@
 from .pygad import * # Relative import.
 
-__version__ = "2.15.1"
+__version__ = "2.16.0"
diff --git a/example_custom_operators.py b/example_custom_operators.py
@@ -0,0 +1,74 @@
+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()
diff --git a/pygad.py b/pygad.py

Original file line number	Diff line number	Diff line change
`@@ -1,3 +1,3 @@`
`1`	`1`	`from .pygad import * # Relative import.`
`2`	`2`
`3`		`-__version__ = "2.15.1"`
	`3`	`+__version__ = "2.16.0"`