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=BinaryTournamentSelection())
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]))
def main() -> None:
problem = ZDT1()
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=BinaryTournamentSelection(RankingAndCrowdingDistanceComparator()))
selection=BinaryTournament2Selection([
SolutionAttributeComparator("dominance_ranking"),
SolutionAttributeComparator("crowding_distance",
lowest_is_best=False)
]))
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))
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())
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())
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=BinaryTournamentSelection())
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]))
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()))
selection = BinaryTournament2([SolutionAttributeComparator("dominance_ranking"),
SolutionAttributeComparator("crowding_distance", lowest_is_best=False)]))
observer = AlgorithmObserver(animation_speed=1*10e-8)
algorithm.observable.register(observer=observer)
algorithm.run()
logger.info("Algorithm (continuous problem): " + algorithm.get_name())
logger.info("Problem: " + problem.get_name())
def main() -> None:
variables = 10
problem = Sphere(variables)
algorithm = GenerationalGeneticAlgorithm[FloatSolution, FloatSolution](
problem=problem,
population_size=100,
max_evaluations=25000,
mutation=Polynomial(probability=1.0 / variables,
distribution_index=20),
crossover=SBX(probability=1.0, distribution_index=20),
selection=BinaryTournamentSelection())
algorithm.run()
result = algorithm.get_result()
print("Algorithm: " + algorithm.get_name())
print("Problem: " + problem.get_name())
print("Solution: " + str(result.variables))
print("Fitness: " + str(result.objectives[0]))
print("Computing time: " + str(algorithm.total_computing_time))
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]))
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))
def main() -> None:
problem = Kursawe()
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=BinaryTournamentSelection())
algorithm.run()
result = algorithm.get_result()
SolutionListOutput[FloatSolution].plot_scatter_to_file(result,
file_name="FUN." +
problem.get_name(),
output_format='eps',
dpi=200)
SolutionListOutput[FloatSolution].plot_scatter_to_screen(result)
logger.info("Algorithm (continuous problem): " + algorithm.get_name())
logger.info("Problem: " + problem.get_name())