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‎Example_GeneticAlgorithm.py

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import numpy
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import GA
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"""
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The y=target is to maximize this equation ASAP:
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y = w1x1+w2x2+w3x3+w4x4+w5x5+6wx6
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where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7)
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What are the best values for the 6 weights w1 to w6?
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We are going to use the genetic algorithm for the best possible values after a number of generations.
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"""
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# Inputs of the equation.
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equation_inputs = [4,-2,3.5,5,-11,-4.7]
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# Number of the weights we are looking to optimize.
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num_weights = 6
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"""
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Genetic algorithm parameters:
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Mating pool size
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Population size
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"""
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sol_per_pop = 8
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num_parents_mating = 4
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# Defining the population size.
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pop_size = (sol_per_pop,num_weights) # The population will have sol_per_pop chromosome where each chromosome has num_weights genes.
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#Creating the initial population.
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new_population = numpy.random.uniform(low=-4.0, high=4.0, size=pop_size)
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print(new_population)
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"""
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new_population[0, :] = [2.4, 0.7, 8, -2, 5, 1.1]
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new_population[1, :] = [-0.4, 2.7, 5, -1, 7, 0.1]
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new_population[2, :] = [-1, 2, 2, -3, 2, 0.9]
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new_population[3, :] = [4, 7, 12, 6.1, 1.4, -4]
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new_population[4, :] = [3.1, 4, 0, 2.4, 4.8, 0]
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new_population[5, :] = [-2, 3, -7, 6, 3, 3]
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"""
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best_outputs = []
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num_generations = 1000
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for generation in range(num_generations):
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print("Generation : ", generation)
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# Measuring the fitness of each chromosome in the population.
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fitness = GA.cal_pop_fitness(equation_inputs, new_population)
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print("Fitness")
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print(fitness)
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# Selecting the best parents in the population for mating.
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parents = GA.select_mating_pool(new_population, fitness,
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num_parents_mating)
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print("Parents")
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print(parents)
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# Generating next generation using crossover.
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offspring_crossover = GA.crossover(parents,
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offspring_size=(pop_size[0]-parents.shape[0], num_weights))
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print("Crossover")
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print(offspring_crossover)
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# Adding some variations to the offsrping using mutation.
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offspring_mutation = GA.mutation(offspring_crossover)
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print("Mutation")
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print(offspring_mutation)
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# Creating the new population based on the parents and offspring.
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new_population[0:parents.shape[0], :] = parents
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new_population[parents.shape[0]:, :] = offspring_mutation
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best_outputs.append(numpy.max(numpy.sum(new_population*equation_inputs, axis=1)))
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# The best result in the current iteration.
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print("Best result : ", numpy.max(numpy.sum(new_population*equation_inputs, axis=1)))
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# Getting the best solution after iterating finishing all generations.
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#At first, the fitness is calculated for each solution in the final generation.
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fitness = GA.cal_pop_fitness(equation_inputs, new_population)
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# Then return the index of that solution corresponding to the best fitness.
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best_match_idx = numpy.where(fitness == numpy.max(fitness))
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print("Best solution : ", new_population[best_match_idx, :])
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print("Best solution fitness : ", fitness[best_match_idx])
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import matplotlib.pyplot
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matplotlib.pyplot.plot(best_outputs)
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matplotlib.pyplot.xlabel("Iteration")
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matplotlib.pyplot.ylabel("Fitness")
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‎GA.py

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import numpy
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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):
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# Mutation changes a single gene in each offspring randomly.
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for idx in range(offspring_crossover.shape[0]):
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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, 4] = offspring_crossover[idx, 4] + random_value
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return offspring_crossover

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