static List<ExperimentAlgorithm<DoubleSolution, List<DoubleSolution>>> configureAlgorithmList(
          List<ExperimentProblem<DoubleSolution>> problemList) {
    List<ExperimentAlgorithm<DoubleSolution, List<DoubleSolution>>> algorithms = new ArrayList<>();
    for (int run = 0; run < INDEPENDENT_RUNS; run++) {

      for (int i = 0; i < problemList.size(); i++) {
        Algorithm<List<DoubleSolution>> algorithm = new NSGAIIBuilder<>(
                problemList.get(i).getProblem(),
                new SBXCrossover(1.0, 20),
                new PolynomialMutation(1.0 / problemList.get(i).getProblem().getNumberOfVariables(), 20.0),
                100)
                .setMaxEvaluations(25000)
                .build();
        algorithms.add(new ExperimentAlgorithm<>(algorithm, problemList.get(i), run));
      }

      for (int i = 0; i < problemList.size(); i++) {
        Algorithm<List<DoubleSolution>> algorithm = new SPEA2Builder<DoubleSolution>(
                problemList.get(i).getProblem(),
                new SBXCrossover(1.0, 10.0),
                new PolynomialMutation(1.0 / problemList.get(i).getProblem().getNumberOfVariables(), 20.0))
                .build();
        algorithms.add(new ExperimentAlgorithm<>(algorithm, problemList.get(i), run));
      }

      for (int i = 0; i < problemList.size(); i++) {
        double mutationProbability = 1.0 / problemList.get(i).getProblem().getNumberOfVariables();
        double mutationDistributionIndex = 20.0;
        Algorithm<List<DoubleSolution>> algorithm = new SMPSOBuilder((DoubleProblem) problemList.get(i).getProblem(),
                new CrowdingDistanceArchive<DoubleSolution>(100))
                .setMutation(new PolynomialMutation(mutationProbability, mutationDistributionIndex))
                .setMaxIterations(250)
                .setSwarmSize(100)
                .setSolutionListEvaluator(new SequentialSolutionListEvaluator<DoubleSolution>())
                .build();
        algorithms.add(new ExperimentAlgorithm<>(algorithm, problemList.get(i), run));
      }
      for (int i = 0; i < problemList.size(); i++) {
        double cr = 0.5;
        double f = 0.5;

        Algorithm<List<DoubleSolution>> algorithm = new GDE3Builder((DoubleProblem) problemList.get(i).getProblem())
                .setCrossover(new DifferentialEvolutionCrossover(cr, f, "rand/1/bin"))
                .setSelection(new DifferentialEvolutionSelection())
                .setMaxEvaluations(25000)
                .setPopulationSize(100)
                .setSolutionSetEvaluator(new SequentialSolutionListEvaluator<>())
                .build();
        algorithms.add(new ExperimentAlgorithm<>(algorithm, problemList.get(i), run));
      }

      for (int i = 0; i < problemList.size(); i++) {
        Algorithm<List<DoubleSolution>> algorithm = new MOCellBuilder<DoubleSolution>(
                (DoubleProblem) problemList.get(i).getProblem(),
                new SBXCrossover(1.0, 20.0),
                new PolynomialMutation(1.0 / problemList.get(i).getProblem().getNumberOfVariables(), 20.0))
                .setSelectionOperator(new BinaryTournamentSelection<>())
                .setMaxEvaluations(25000)
                .setPopulationSize(100)
                .setArchive(new CrowdingDistanceArchive<DoubleSolution>(100))
                .build();
        algorithms.add(new ExperimentAlgorithm<>(algorithm, problemList.get(i), run));
      }
    }
    return algorithms;
  }
static List < ExperimentAlgorithm < DoubleSolution , List < DoubleSolution > > > configureAlgorithmList ( List < ExperimentProblem < DoubleSolution > > problemList ) { List < ExperimentAlgorithm < DoubleSolution , List < DoubleSolution > > > algorithms = new ArrayList < > ( ) ; for ( int run = 0 ; run < INDEPENDENT_RUNS ; run ++ ) { for ( int i = 0 ; i < problemList . size ( ) ; i ++ ) { Algorithm < List < DoubleSolution > > algorithm = new NSGAIIBuilder < > ( problemList . get ( i ) . getProblem ( ) , new SBXCrossover ( 1.0 , 20 ) , new PolynomialMutation ( 1.0 / problemList . get ( i ) . getProblem ( ) . getNumberOfVariables ( ) , 20.0 ) , 100 ) . setMaxEvaluations ( 25000 ) . build ( ) ; algorithms . add ( new ExperimentAlgorithm < > ( algorithm , problemList . get ( i ) , run ) ) ; } for ( int i = 0 ; i < problemList . size ( ) ; i ++ ) { Algorithm < List < DoubleSolution > > algorithm = new SPEA2Builder < DoubleSolution > ( problemList . get ( i ) . getProblem ( ) , new SBXCrossover ( 1.0 , 10.0 ) , new PolynomialMutation ( 1.0 / problemList . get ( i ) . getProblem ( ) . getNumberOfVariables ( ) , 20.0 ) ) . build ( ) ; algorithms . add ( new ExperimentAlgorithm < > ( algorithm , problemList . get ( i ) , run ) ) ; } for ( int i = 0 ; i < problemList . size ( ) ; i ++ ) { double mutationProbability = 1.0 / problemList . get ( i ) . getProblem ( ) . getNumberOfVariables ( ) ; double mutationDistributionIndex = 20.0 ; Algorithm < List < DoubleSolution > > algorithm = new SMPSOBuilder ( ( DoubleProblem ) problemList . get ( i ) . getProblem ( ) , new CrowdingDistanceArchive < DoubleSolution > ( 100 ) ) . setMutation ( new PolynomialMutation ( mutationProbability , mutationDistributionIndex ) ) . setMaxIterations ( 250 ) . setSwarmSize ( 100 ) . setSolutionListEvaluator ( new SequentialSolutionListEvaluator < DoubleSolution > ( ) ) . build ( ) ; algorithms . add ( new ExperimentAlgorithm < > ( algorithm , problemList . get ( i ) , run ) ) ; } for ( int i = 0 ; i < problemList . size ( ) ; i ++ ) { double cr = 0.5 ; double f = 0.5 ; Algorithm < List < DoubleSolution > > algorithm = new GDE3Builder ( ( DoubleProblem ) problemList . get ( i ) . getProblem ( ) ) . setCrossover ( new DifferentialEvolutionCrossover ( cr , f , "rand/1/bin" ) ) . setSelection ( new DifferentialEvolutionSelection ( ) ) . setMaxEvaluations ( 25000 ) . setPopulationSize ( 100 ) . setSolutionSetEvaluator ( new SequentialSolutionListEvaluator < > ( ) ) . build ( ) ; algorithms . add ( new ExperimentAlgorithm < > ( algorithm , problemList . get ( i ) , run ) ) ; } for ( int i = 0 ; i < problemList . size ( ) ; i ++ ) { Algorithm < List < DoubleSolution > > algorithm = new MOCellBuilder < DoubleSolution > ( ( DoubleProblem ) problemList . get ( i ) . getProblem ( ) , new SBXCrossover ( 1.0 , 20.0 ) , new PolynomialMutation ( 1.0 / problemList . get ( i ) . getProblem ( ) . getNumberOfVariables ( ) , 20.0 ) ) . setSelectionOperator ( new BinaryTournamentSelection < > ( ) ) . setMaxEvaluations ( 25000 ) . setPopulationSize ( 100 ) . setArchive ( new CrowdingDistanceArchive < DoubleSolution > ( 100 ) ) . build ( ) ; algorithms . add ( new ExperimentAlgorithm < > ( algorithm , problemList . get ( i ) , run ) ) ; } } return algorithms ; }
The algorithm list is composed of pairs {@link Algorithm} + {@link Problem} which form part of
a {@link ExperimentAlgorithm}, which is a decorator for class {@link Algorithm}. The {@link
ExperimentAlgorithm} has an optional tag component, that can be set as it is shown in this example,
where four variants of a same algorithm are defined.