import numpy import ga import matplotlib.pyplot """ The y=target is to maximize this equation ASAP: y = w1x1+w2x2+w3x3+w4x4+w5x5+6wx6 where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7) What are the best values for the 6 weights w1 to w6? We are going to use the genetic algorithm for the best possible values after a number of generations. """ # Inputs of the equation. equation_inputs = [0.153,0.068,0.008,0.053,0.073,0.039,0.047,0.005,0.011] # Number of the weights we are looking to optimize. num_weights = len(equation_inputs) """ Genetic algorithm parameters: Mating pool size Population size """ sol_per_pop = 16 # matching pool size num_parents_mating = 2 # population size , or the number of parents mating # Defining the population size. pop_size = (sol_per_pop,num_weights) # The population will have sol_per_pop chromosome where each chromosome has num_weights genes. print("pop size is",pop_size) #Creating the initial population. new_population = numpy.random.uniform(low=-4.0, high=4.0, size=pop_size) print(new_population) best_outputs = [] num_generations = 5000 for generation in range(num_generations): print("Generation : ", generation) # Measuring the fitness of each chromosome in the population. fitness = ga.cal_pop_fitness(equation_inputs, new_population) print("Fitness") print(fitness) best_outputs.append(numpy.max(numpy.sum(new_population*equation_inputs, axis=1))) # The best result in the current iteration. print("Best result : ", numpy.max(numpy.sum(new_population*equation_inputs, axis=1))) # Selecting the best parents in the population for mating. parents = ga.select_mating_pool(new_population, fitness, num_parents_mating) print("Parents") print(parents) # Generating next generation using crossover. offspring_crossover = ga.crossover(parents, offspring_size=(pop_size[0]-parents.shape[0], num_weights)) print("Crossover") print(offspring_crossover) # Adding some variations to the offspring using mutation. offspring_mutation = ga.mutation(offspring_crossover, num_mutations=2) print("Mutation") print(offspring_mutation) # Creating the new population based on the parents and offspring. new_population[0:parents.shape[0], :] = parents new_population[parents.shape[0]:, :] = offspring_mutation # Getting the best solution after iterating finishing all generations. #At first, the fitness is calculated for each solution in the final generation. fitness = ga.cal_pop_fitness(equation_inputs, new_population) # Then return the index of that solution corresponding to the best fitness. best_match_idx = numpy.where(fitness == numpy.max(fitness)) print("Best solution : ", new_population[best_match_idx, :]) print("Best solution fitness : ", fitness[best_match_idx]) #Graph goes here matplotlib.pyplot.plot(best_outputs) matplotlib.pyplot.xlabel("Iteration") matplotlib.pyplot.ylabel("Fitness") matplotlib.pyplot.show()