Lista de todas las funciones con enlaces a las clases a las que pertenecen:

- s -

• sampling() : evolutionary_algorithms.complement.ProbabilisticSampling, evolutionary_algorithms.complement.Sampling
• searchState() : metaheuristics.generators.MultiGenerator
• SecureRandomGenerator() : config.SecureRandomGenerator
• selection() : evolutionary_algorithms.complement.FatherSelection, evolutionary_algorithms.complement.RouletteSelection, evolutionary_algorithms.complement.TruncationSelection
• setActiveGenerator() : metaheuristics.generators.MultiGenerator
• setBestState() : metaheurictics.strategy.Strategy
• setCode() : problem.definition.State
• setCodification() : problem.definition.Problem
• setCountCurrent() : metaheurictics.strategy.Strategy
• setCountMax() : metaheurictics.strategy.Strategy
• setCountRef() : metaheuristics.generators.ParticleSwarmOptimization
• setData() : evolutionary_algorithms.complement.Range
• setDistributionType() : metaheuristics.generators.DistributionEstimationAlgorithm
• setEvaluation() : problem.definition.State
• setFactorySolutionMethod() : problem.definition.Problem
• setFunction() : problem.definition.Problem
• setGenerator() : metaheuristics.generators.LimitRoulette
• setGeneratorType() : metaheuristics.generators.DistributionEstimationAlgorithm, metaheuristics.generators.GeneticAlgorithm, metaheuristics.generators.HillClimbing, metaheuristics.generators.HillClimbingRestart, metaheuristics.generators.MultiobjectiveHillClimbingDistance, metaheuristics.generators.MultiobjectiveHillClimbingRestart, metaheuristics.generators.MultiobjectiveStochasticHillClimbing, metaheuristics.generators.ParticleSwarmOptimization
• setGeneratortype() : metaheuristics.generators.MultiGenerator
• setIdCity() : config.tspDynamic.TSPState
• setInitialReference() : metaheuristics.generators.DistributionEstimationAlgorithm, metaheuristics.generators.EvolutionStrategies, metaheuristics.generators.Generator, metaheuristics.generators.GeneticAlgorithm, metaheuristics.generators.HillClimbing, metaheuristics.generators.HillClimbingRestart, metaheuristics.generators.LimitThreshold, metaheuristics.generators.MultiCaseSimulatedAnnealing, metaheuristics.generators.MultiGenerator, metaheuristics.generators.MultiobjectiveHillClimbingDistance, metaheuristics.generators.MultiobjectiveHillClimbingRestart, metaheuristics.generators.MultiobjectiveStochasticHillClimbing, metaheuristics.generators.MultiobjectiveTabuSearch, metaheuristics.generators.Particle, metaheuristics.generators.ParticleSwarmOptimization, metaheuristics.generators.RandomSearch, metaheuristics.generators.SimulatedAnnealing, metaheuristics.generators.TabuSearch
• setKey() : evolutionary_algorithms.complement.Probability
• setLimitHigh() : metaheuristics.generators.LimitRoulette
• setLimitLow() : metaheuristics.generators.LimitRoulette
• setListGeneratedPP() : metaheuristics.generators.MultiGenerator
• setListGenerators() : metaheuristics.generators.MultiGenerator
• setListParticle() : metaheuristics.generators.ParticleSwarmOptimization
• setListReference() : metaheuristics.generators.DistributionEstimationAlgorithm
• setListState() : metaheuristics.generators.GeneticAlgorithm
• setListStateReference() : metaheuristics.generators.EvolutionStrategies, metaheuristics.generators.ParticleSwarmOptimization
• setMax() : evolutionary_algorithms.complement.Range
• setMin() : evolutionary_algorithms.complement.Range
• setNumber() : problem.definition.State
• setOperator() : problem.definition.Problem
• setPossibleValue() : problem.definition.Problem
• setProbability() : evolutionary_algorithms.complement.Probability
• setProblem() : metaheurictics.strategy.Strategy
• setState() : problem.definition.Problem
• setStateActual() : metaheuristics.generators.Particle
• setStatePBest() : metaheuristics.generators.Particle
• setStateRef() : metaheuristics.generators.EvolutionStrategies, metaheuristics.generators.GeneticAlgorithm, metaheuristics.generators.HillClimbing, metaheuristics.generators.HillClimbingRestart, metaheuristics.generators.LimitThreshold, metaheuristics.generators.MultiCaseSimulatedAnnealing, metaheuristics.generators.MultiobjectiveHillClimbingDistance, metaheuristics.generators.MultiobjectiveHillClimbingRestart, metaheuristics.generators.MultiobjectiveStochasticHillClimbing, metaheuristics.generators.MultiobjectiveTabuSearch, metaheuristics.generators.SimulatedAnnealing, metaheuristics.generators.TabuSearch
• setStateReferencePSO() : metaheuristics.generators.ParticleSwarmOptimization
• setStateReferenceTS() : metaheuristics.generators.MultiobjectiveTabuSearch
• setStopexecute() : metaheurictics.strategy.Strategy
• setTabusolution() : local_search.candidate_type.CandidateValue
• setTerminate() : metaheuristics.generators.InstanceDE, metaheuristics.generators.InstanceEE, metaheuristics.generators.InstanceGA
• setThreshold() : metaheurictics.strategy.Strategy
• setTypeCandidate() : metaheuristics.generators.HillClimbing, metaheuristics.generators.HillClimbingRestart, metaheuristics.generators.LimitThreshold, metaheuristics.generators.MultiobjectiveTabuSearch, metaheuristics.generators.TabuSearch
• setTypeGenerator() : metaheuristics.generators.EvolutionStrategies, metaheuristics.generators.LimitThreshold, metaheuristics.generators.MultiCaseSimulatedAnnealing, metaheuristics.generators.MultiobjectiveTabuSearch, metaheuristics.generators.RandomSearch, metaheuristics.generators.SimulatedAnnealing, metaheuristics.generators.TabuSearch, problem.definition.State
• setTypeProblem() : problem.definition.ObjetiveFunction, problem.definition.Problem
• setTypeSolutionMethod() : problem.definition.Problem
• setUpdateparameter() : metaheurictics.strategy.Strategy
• setValue() : config.tspDynamic.TSPState, evolutionary_algorithms.complement.Probability
• setVelocity() : metaheuristics.generators.Particle
• setWeight() : metaheuristics.generators.DistributionEstimationAlgorithm, metaheuristics.generators.EvolutionStrategies, metaheuristics.generators.Generator, metaheuristics.generators.GeneticAlgorithm, metaheuristics.generators.HillClimbing, metaheuristics.generators.HillClimbingRestart, metaheuristics.generators.LimitThreshold, metaheuristics.generators.MultiCaseSimulatedAnnealing, metaheuristics.generators.MultiGenerator, metaheuristics.generators.MultiobjectiveHillClimbingDistance, metaheuristics.generators.MultiobjectiveHillClimbingRestart, metaheuristics.generators.MultiobjectiveStochasticHillClimbing, metaheuristics.generators.MultiobjectiveTabuSearch, metaheuristics.generators.Particle, metaheuristics.generators.ParticleSwarmOptimization, metaheuristics.generators.RandomSearch, metaheuristics.generators.SimulatedAnnealing, metaheuristics.generators.TabuSearch, problem.definition.ObjetiveFunction
• SimulatedAnnealing() : metaheuristics.generators.SimulatedAnnealing
• SolutionMoreDistance() : metaheuristics.generators.MultiobjectiveHillClimbingDistance
• sortedPathValue() : evolutionary_algorithms.complement.AIOMutation
• State() : problem.definition.State
• stateCandidate() : local_search.candidate_type.CandidateValue
• stateSearch() : local_search.candidate_type.GreaterCandidate, local_search.candidate_type.NotDominatedCandidate, local_search.candidate_type.RandomCandidate, local_search.candidate_type.SearchCandidate, local_search.candidate_type.SmallerCandidate
• stopIterations() : local_search.complement.StopExecute
• Strategy() : metaheurictics.strategy.Strategy