Genetic Algorithm

class jmetal.algorithm.singleobjective.genetic_algorithm.GeneticAlgorithm(problem: ~jmetal.core.problem.Problem, population_size: int, offspring_population_size: int, mutation: ~jmetal.core.operator.Mutation, crossover: ~jmetal.core.operator.Crossover, selection: ~jmetal.core.operator.Selection = <jmetal.operator.selection.BinaryTournamentSelection object>, termination_criterion: ~jmetal.util.termination_criterion.TerminationCriterion = <jmetal.util.termination_criterion.StoppingByEvaluations object>, population_generator: ~jmetal.util.generator.Generator = <jmetal.util.generator.RandomGenerator object>, population_evaluator: ~jmetal.util.evaluator.Evaluator = <jmetal.util.evaluator.SequentialEvaluator object>, solution_comparator: ~jmetal.util.comparator.Comparator = <jmetal.util.comparator.ObjectiveComparator object>)

Bases: EvolutionaryAlgorithm[S, R]

create_initial_solutions() → List[S]

Creates the initial list of solutions of a metaheuristic.

evaluate(population: List[S])

Evaluates a solution list.

get_name() → str

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

Replace least-fit population with new individuals.

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

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

result() → R

selection(population: List[S])

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

stopping_condition_is_met() → bool

The stopping condition is met or not.