def plus_algo(add_e_evaluation, add_v_variation, add_s_selection):
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
evo_alg = MuPlusLambda(add_e_evaluation, add_s_selection, crossover,
mutation, 0.2, 0.4, 20)
evo_alg.variation = add_v_variation
return evo_alg
def evol_alg():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
selection = Tournament(SELECTION_SIZE)
fitness = MultipleValueFitnessFunction()
evaluator = Evaluation(fitness)
return MuPlusLambda(evaluator, selection, crossover, mutation, 0.2, 0.4,
OFFSPRING_SIZE)
def ev_alg():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(np.random.random)
selection = Tournament(2)
training_data = np.linspace(0.1, 1, FULL_TRAINING_DATA_SIZE)
fitness = DistanceToAverage(training_data)
evaluator = Evaluation(fitness)
return MuPlusLambda(evaluator, selection, crossover, mutation,
0., 1.0, MAIN_POPULATION_SIZE)
def island():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
selection = Tournament(10)
fitness = MultipleValueFitnessFunction()
evaluator = Evaluation(fitness)
ev_alg = MuPlusLambda(evaluator, selection, crossover, mutation, 0.2, 0.4,
20)
generator = MultipleValueChromosomeGenerator(mutation_function, 10)
return Island(ev_alg, generator, 25)
def main():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(get_random_float)
selection = Tournament(10)
fitness = ZeroMinFitnessFunction()
local_opt_fitness = ContinuousLocalOptimization(fitness)
evaluator = Evaluation(local_opt_fitness)
ea = MuPlusLambda(evaluator, selection, crossover, mutation, 0.4, 0.4, 20)
generator = MultipleFloatChromosomeGenerator(get_random_float, 8)
island = Island(ea, generator, 25)
best_indv_values = []
best_indv_values.append(island.best_individual().values)
for i in range(500):
island.execute_generational_step()
best_indv_values.append(island.best_individual().values)
bingo.animation.animate_data(best_indv_values)
def main():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(get_random_float)
selection = Tournament(10)
fitness = ZeroMinFitnessFunction()
local_opt_fitness = ContinuousLocalOptimization(fitness)
evaluator = Evaluation(local_opt_fitness)
ea = MuPlusLambda(evaluator, selection, crossover, mutation, 0.4, 0.4, 20)
generator = MultipleFloatChromosomeGenerator(get_random_float, 8)
island = Island(ea, generator, 25)
for i in range(25):
island.execute_generational_step()
print("\nGeneration #", i)
print("-" * 80, "\n")
report_max_min_mean_fitness(island.population)
print("\npopulation: \n")
for indv in island.population:
print(["{0:.2f}".format(val) for val in indv.values])