Exemplos de BinaryTournament em Python

Exemplo n.º 1

def main() -> None:
    class NSGA2b(NSGAII[S, R]):
        def is_stopping_condition_reached(self):
            # Re-define the stopping condition
            reached = [False, True][self.get_current_computing_time() > 4]

            if reached:
                logger.info("Stopping condition reached!")

            return reached

    problem = Fonseca()
    algorithm = NSGA2b[FloatSolution, List[FloatSolution]](
        problem,
        population_size=100,
        max_evaluations=25000,
        mutation=Polynomial(1.0 / problem.number_of_variables,
                            distribution_index=20),
        crossover=SBX(1.0, distribution_index=20),
        selection=BinaryTournament(RankingAndCrowdingDistanceComparator()))

    algorithm.run()
    result = algorithm.get_result()

    SolutionListOutput[FloatSolution].print_function_values_to_file(
        "FUN." + problem.get_name(), result)

    logger.info("Algorithm (continuous problem): " + algorithm.get_name())
    logger.info("Problem: " + problem.get_name())

Exemplo n.º 2

Arquivo: generational_genetic_algorithm_stopping_by_time.py Projeto: minghao2016/jMetalPy

def main() -> None:
    class GGA2(GenerationalGeneticAlgorithm[FloatSolution, FloatSolution]):
        def is_stopping_condition_reached(self):
            # Re-define the stopping condition
            reached = [False, True][self.get_current_computing_time() > 4]

            if reached:
                logger.info("Stopping condition reached!")

            return reached

    variables = 10
    problem = Sphere(variables)
    algorithm = GGA2(problem,
                     population_size=100,
                     max_evaluations=0,
                     mutation=Polynomial(1.0 / variables,
                                         distribution_index=20),
                     crossover=SBX(1.0, distribution_index=20),
                     selection=BinaryTournament())

    algorithm.run()
    result = algorithm.get_result()

    logger.info("Algorithm (stop for timeout): " + algorithm.get_name())
    logger.info("Problem: " + problem.get_name())
    logger.info("Solution: " + str(result.variables))
    logger.info("Fitness:  " + str(result.objectives[0]))

Exemplo n.º 3

def main() -> None:
    problem = Kursawe()
    algorithm = NSGAII[FloatSolution, List[FloatSolution]](
        problem=problem,
        population_size=100,
        max_evaluations=25000,
        mutation=Polynomial(1.0/problem.number_of_variables, distribution_index=20),
        crossover=SBX(1.0, distribution_index=20),
        selection = BinaryTournament(RankingAndCrowdingDistanceComparator()))

    algorithm.run()
    result = algorithm.get_result()

    SolutionListOutput[FloatSolution].print_function_values_to_file("FUN."+problem.get_name(), result)

    logger.info("Algorithm (continuous problem): " + algorithm.get_name())
    logger.info("Problem: " + problem.get_name())

Exemplo n.º 4

Arquivo: generational_genetic_algorithm_float.py Projeto: minghao2016/jMetalPy

def main() -> None:
    variables = 10
    problem = Sphere(variables)
    algorithm = GenerationalGeneticAlgorithm[FloatSolution, FloatSolution](
        problem,
        population_size=100,
        max_evaluations=25000,
        mutation=Polynomial(1.0/variables, distribution_index=20),
        crossover=SBX(1.0, distribution_index=20),
        selection=BinaryTournament())

    algorithm.run()
    result = algorithm.get_result()

    logger.info("Algorithm (continuous problem): " + algorithm.get_name())
    logger.info("Problem: " + problem.get_name())
    logger.info("Solution: " + str(result.variables))
    logger.info("Fitness:  " + str(result.objectives[0]))

Exemplo n.º 5

def main() -> None:
    variables = 10
    problem = Sphere(variables)
    algorithm = GenerationalGeneticAlgorithm[FloatSolution, FloatSolution](
        problem,
        population_size=100,
        max_evaluations=25000,
        mutation=Polynomial(1.0 / variables, distribution_index=20),
        crossover=SBX(1.0, distribution_index=20),
        selection=BinaryTournament())

    observer = BasicAlgorithmConsumer(2000)

    algorithm.observable.register(observer=observer)

    algorithm.start()
    algorithm.join()

    result = algorithm.get_result()
    logger.info("Algorithm: " + algorithm.get_name())
    logger.info("Problem: " + problem.get_name())
    logger.info("Solution: " + str(result.variables))
    logger.info("Fitness:  " + str(result.objectives[0]))

Exemplo n.º 6

def main() -> None:
    problem = ZDT1()
    algorithm = NSGAII[FloatSolution, List[FloatSolution]](
        problem,
        population_size = 100,
        max_evaluations = 25000,
        mutation = Polynomial(1.0/problem.number_of_variables, distribution_index=20),
        crossover = SBX(1.0, distribution_index=20),
        selection = BinaryTournament(RankingAndCrowdingDistanceComparator()))

    observer = BasicAlgorithmConsumer(1000)

    algorithm.observable.register(observer=observer)

    algorithm.run()

    result = algorithm.get_result()

    SolutionListOutput[FloatSolution].print_function_values_to_file("FUN."+problem.get_name(), result)

    logger.info("Algorithm (continuous problem): " + algorithm.get_name())
    logger.info("Problem: " + problem.get_name())
    logger.info("Computing time: " + str(algorithm.total_computing_time))