GDE3

Example

from jmetal.algorithm.multiobjective.gde3 import GDE3
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations

problem = ZDT1()

max_evaluations = 25000

algorithm = GDE3(
    problem=problem,
    population_size=100,
    cr=0.5,
    f=0.5,
    termination_criterion=StoppingByEvaluations(max_evaluations)
)

algorithm.run()
solutions = algorithm.get_result()

We can now visualize the Pareto front approximation:

from jmetal.lab.visualization.plotting import Plot
from jmetal.util.solution import get_non_dominated_solutions

front = get_non_dominated_solutions(solutions)

plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='GDE3-ZDT1')

API

class jmetal.algorithm.multiobjective.gde3.GDE3(problem: ~jmetal.core.problem.Problem, population_size: int, cr: float, f: float, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, k: float = 0.5, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, dominance_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.DominanceComparator object>)

Bases: EvolutionaryAlgorithm[FloatSolution, FloatSolution]

create_initial_solutions() → List[FloatSolution]

Creates the initial list of solutions of a metaheuristic.

evaluate(solution_list: List[FloatSolution]) → List[FloatSolution]

Evaluates a solution list.

get_name() → str

replacement(population: List[S], offspring_population: List[FloatSolution]) → List[List[FloatSolution]]

Replace least-fit population with new individuals.

reproduction(mating_pool: List[S]) → List[S]

Breed new individuals through crossover and mutation operations to give birth to offspring.

result() → List[FloatSolution]

selection(population: List[FloatSolution]) → List[FloatSolution]

Select the best-fit individuals for reproduction (parents).

stopping_condition_is_met() → bool

The stopping condition is met or not.