53 lines
2.4 KiB
Python
53 lines
2.4 KiB
Python
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import numpy
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# Genetic Algorithm methods
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x = "Hello world"
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def cal_pop_fitness(equation_inputs, pop):
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# Calculating the fitness value of each solution in the current population.
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# The fitness function calulates the sum of products between each input and its corresponding weight.
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fitness = numpy.sum(pop * equation_inputs, axis=1)
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return fitness
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def select_mating_pool(pop, fitness, num_parents):
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# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.
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parents = numpy.empty((num_parents, pop.shape[1]))
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for parent_num in range(num_parents):
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max_fitness_idx = numpy.where(fitness == numpy.max(fitness))
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max_fitness_idx = max_fitness_idx[0][0]
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parents[parent_num, :] = pop[max_fitness_idx, :]
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fitness[max_fitness_idx] = -99999999999
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return parents
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def crossover(parents, offspring_size):
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offspring = numpy.empty(offspring_size)
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# The point at which crossover takes place between two parents. Usually, it is at the center.
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crossover_point = numpy.uint8(offspring_size[1] / 2)
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for k in range(offspring_size[0]):
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# Index of the first parent to mate.
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parent1_idx = k % parents.shape[0]
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# Index of the second parent to mate.
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parent2_idx = (k + 1) % parents.shape[0]
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# The new offspring will have its first half of its genes taken from the first parent.
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offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]
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# The new offspring will have its second half of its genes taken from the second parent.
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offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]
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return offspring
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def mutation(offspring_crossover, num_mutations=1):
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mutations_counter = numpy.uint8(offspring_crossover.shape[1] / num_mutations)
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# Mutation changes a number of genes as defined by the num_mutations argument. The changes are random.
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for idx in range(offspring_crossover.shape[0]):
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gene_idx = mutations_counter - 1
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for mutation_num in range(num_mutations):
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# The random value to be added to the gene.
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random_value = numpy.random.uniform(-1.0, 1.0, 1)
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offspring_crossover[idx, gene_idx] = offspring_crossover[idx, gene_idx] + random_value
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gene_idx = gene_idx + mutations_counter
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return offspring_crossover
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