if __name__ == '__main__':
problem = Rastrigin(10)
max_evaluations = 50000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20.0),
crossover=SBXCrossover(probability=0.9, distribution_index=20.0),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
dominance_comparator=DominanceComparator())
algorithm.observable.register(observer=PrintObjectivesObserver(1000))
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(
front, 'FUN.' + algorithm.get_name() + "-" + problem.get_name())
print_variables_to_file(
front, 'VAR.' + algorithm.get_name() + "-" + problem.get_name())
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.algorithm.singleobjective.local_search import LocalSearch
from jmetal.operator import PolynomialMutation
from jmetal.problem.singleobjective.unconstrained import Rastrigin
from jmetal.util.solution import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == "__main__":
problem = Rastrigin(10)
max_evaluations = 100000
algorithm = LocalSearch(
problem=problem,
mutation=PolynomialMutation(1.0 / problem.number_of_variables, 20.0),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
)
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(result, "FUN." + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(result, "VAR." + algorithm.get_name() + "." + problem.get_name())
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))
if __name__ == '__main__':
problem = Rastrigin(10)
max_evaluations = 250000
algorithm = SimulatedAnnealing(
problem=problem,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20.0),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
objectives_observer = PrintObjectivesObserver(frequency=1000)
algorithm.observable.register(observer=objectives_observer)
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(
result, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
result, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
print('Algorithm: ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Solution: ' + str(result.variables[0]))
print('Fitness: ' + str(result.objectives[0]))
print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.problem.singleobjective.unconstrained import Rastrigin
from jmetal.util.observer import PrintObjectivesObserver
from jmetal.util.solution_list import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = Rastrigin(10)
max_evaluations = 100000
algorithm = LocalSearch(
problem=problem,
mutation=PolynomialMutation(1.0 / problem.number_of_variables, 20.0),
termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
objectives_observer = PrintObjectivesObserver(frequency=1000)
algorithm.observable.register(observer=objectives_observer)
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(result, 'FUN.'+ algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(result, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
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))
from jmetal.operator import PolynomialMutation, SBXCrossover
from jmetal.problem.singleobjective.unconstrained import Rastrigin
from jmetal.util.termination_criterion import StoppingByEvaluations
from main import Emas
if __name__ == '__main__':
problem = Rastrigin(10)
algorithm = Emas(
number_of_islands=10,
init_island_population_size=20,
problem=problem,
mutation=PolynomialMutation(1.0 / problem.number_of_variables, 0.2),
crossover=SBXCrossover(0.9, 20.0),
termination_criterion=StoppingByEvaluations(max_evaluations=20000))
algorithm.run()
result = algorithm.get_result()
print('Algorithm: {}'.format(algorithm.get_name()))
print('Problem: {}'.format(problem.get_name()))
for x in result:
for z in x:
print('Solution: {}'.format(z.solution.variables))
print('Fitness: {}'.format(z.solution.objectives[0]))
print('Energy: {}'.format(z.energy))
print('Computing time: {}'.format(algorithm.total_computing_time))
print(len(result))
print("Best fitness: " + str(min([z.fitness() for x in result
for z in x])))
from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
from jmetal.operator import BinaryTournamentSelection, PolynomialMutation, SBXCrossover
from jmetal.problem.singleobjective.unconstrained import Rastrigin
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == "__main__":
problem = Rastrigin(10)
algorithm = GeneticAlgorithm(
problem=problem,
population_size=100,
offspring_population_size=1,
mutation=PolynomialMutation(1.0 / problem.number_of_variables, 20.0),
crossover=SBXCrossover(0.9, 5.0),
selection=BinaryTournamentSelection(),
termination_criterion=StoppingByEvaluations(max_evaluations=100000),
)
algorithm.run()
result = algorithm.get_result()
print("Algorithm: {}".format(algorithm.get_name()))
print("Problem: {}".format(problem.get_name()))
print("Solution: {}".format(result.variables))
print("Fitness: {}".format(result.objectives[0]))
print("Computing time: {}".format(algorithm.total_computing_time))
if __name__ == "__main__":
problem = Rastrigin(10)
max_evaluations = 50000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20.0),
crossover=SBXCrossover(probability=0.9, distribution_index=20.0),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
dominance_comparator=DominanceComparator(),
)
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(
front, "FUN." + algorithm.get_name() + "-" + problem.get_name())
print_variables_to_file(
front, "VAR." + algorithm.get_name() + "-" + problem.get_name())
print("Algorithm (continuous problem): " + algorithm.get_name())
print("Problem: " + problem.get_name())
print("Computing time: " + str(algorithm.total_computing_time))
]
algorithm = SocioCognitiveEvolutionStrategy(
problem=problem,
strategies_params=strategies_params,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
algorithm_normal = EvolutionStrategyWithHistory(
problem=problem,
mu=mu,
lambda_=lambda_,
elitist=elitist,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
algorithm.run()
result = algorithm.get_result()
print('Algorithm: ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Solution: ' + str(result.variables[0]))
print('Fitness: ' + str(result.objectives[0]))
print('Computing time: ' + str(algorithm.total_computing_time))
print(result.variables)
algorithm_normal.run()
draw_comparision_plot(algorithm, algorithm_normal)
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == "__main__":
problem = Rastrigin(10)
max_evaluations = 100000
algorithm = SimulatedAnnealing(
problem=problem,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20.0),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
)
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(
result, "FUN." + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
result, "VAR." + algorithm.get_name() + "." + problem.get_name())
print("Algorithm: " + algorithm.get_name())
print("Problem: " + problem.get_name())
print("Solution: " + str(result.variables[0]))
print("Fitness: " + str(result.objectives[0]))
print("Computing time: " + str(algorithm.total_computing_time))
