/**
   * Returns a new {@link DBEA} instance.
   *
   * @param properties the properties for customizing the new {@code DBEA} instance
   * @param problem the problem
   * @return a new {@code DBEA} instance
   */
  private Algorithm newDBEA(TypedProperties properties, Problem problem) {
    int divisionsOuter = 4;
    int divisionsInner = 0;

    if (properties.contains("divisionsOuter") && properties.contains("divisionsInner")) {
      divisionsOuter = (int) properties.getDouble("divisionsOuter", 4);
      divisionsInner = (int) properties.getDouble("divisionsInner", 0);
    } else if (properties.contains("divisions")) {
      divisionsOuter = (int) properties.getDouble("divisions", 4);
    } else if (problem.getNumberOfObjectives() == 1) {
      divisionsOuter = 100;
    } else if (problem.getNumberOfObjectives() == 2) {
      divisionsOuter = 99;
    } else if (problem.getNumberOfObjectives() == 3) {
      divisionsOuter = 12;
    } else if (problem.getNumberOfObjectives() == 4) {
      divisionsOuter = 8;
    } else if (problem.getNumberOfObjectives() == 5) {
      divisionsOuter = 6;
    } else if (problem.getNumberOfObjectives() == 6) {
      divisionsOuter = 4;
      divisionsInner = 1;
    } else if (problem.getNumberOfObjectives() == 7) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 8) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 9) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 10) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else {
      divisionsOuter = 2;
      divisionsInner = 1;
    }

    int populationSize =
        (int)
            (CombinatoricsUtils.binomialCoefficient(
                    problem.getNumberOfObjectives() + divisionsOuter - 1, divisionsOuter)
                + (divisionsInner == 0
                    ? 0
                    : CombinatoricsUtils.binomialCoefficient(
                        problem.getNumberOfObjectives() + divisionsInner - 1, divisionsInner)));

    Initialization initialization = new RandomInitialization(problem, populationSize);

    Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);

    return new DBEA(problem, initialization, variation, divisionsOuter, divisionsInner);
  }
  /**
   * Returns a new {@link MOEAD} instance. Only real encodings are supported.
   *
   * @param properties the properties for customizing the new {@code MOEAD} instance
   * @param problem the problem
   * @return a new {@code MOEAD} instance
   * @throws FrameworkException if the decision variables are not real valued
   */
  private Algorithm newMOEAD(TypedProperties properties, Problem problem) {
    if (!checkType(RealVariable.class, problem)) {
      throw new FrameworkException("unsupported decision variable type");
    }

    int populationSize = (int) properties.getDouble("populationSize", 100);

    // enforce population size lower bound
    if (populationSize < problem.getNumberOfObjectives()) {
      System.err.println("increasing MOEA/D population size");
      populationSize = problem.getNumberOfObjectives();
    }

    Initialization initialization = new RandomInitialization(problem, populationSize);

    Variation variation = OperatorFactory.getInstance().getVariation("de+pm", properties, problem);

    int neighborhoodSize = 20;
    int eta = 2;

    if (properties.contains("neighborhoodSize")) {
      neighborhoodSize =
          Math.max(2, (int) (properties.getDouble("neighborhoodSize", 0.1) * populationSize));
    }

    if (neighborhoodSize > populationSize) {
      neighborhoodSize = populationSize;
    }

    if (properties.contains("eta")) {
      eta = Math.max(2, (int) (properties.getDouble("eta", 0.01) * populationSize));
    }

    MOEAD algorithm =
        new MOEAD(
            problem,
            neighborhoodSize,
            initialization,
            variation,
            properties.getDouble("delta", 0.9),
            eta,
            (int) properties.getDouble("updateUtility", -1));

    return algorithm;
  }
  /**
   * Returns a new single-objective {@link RepeatedSingleObjective} instance.
   *
   * @param properties the properties for customizing the new {@code RepeatedSingleObjective}
   *     instance
   * @param problem the problem
   * @return a new {@code RepeatedSingleObjective} instance
   */
  private Algorithm newRSO(TypedProperties properties, Problem problem) {
    String algorithmName = properties.getString("algorithm", "GA");
    int instances = (int) properties.getDouble("instances", 100);

    if (!properties.contains("method")) {
      properties.setString("method", "min-max");
    }

    return new RepeatedSingleObjective(
        problem, algorithmName, properties.getProperties(), instances);
  }
  /**
   * Returns a new {@link RandomSearch} instance.
   *
   * @param properties the properties for customizing the new {@code RandomSearch} instance
   * @param problem the problem
   * @return a new {@code RandomSearch} instance
   */
  private Algorithm newRandomSearch(TypedProperties properties, Problem problem) {
    int populationSize = (int) properties.getDouble("populationSize", 100);

    Initialization generator = new RandomInitialization(problem, populationSize);

    NondominatedPopulation archive = null;

    if (properties.contains("epsilon")) {
      archive =
          new EpsilonBoxDominanceArchive(
              properties.getDoubleArray(
                  "epsilon", new double[] {EpsilonHelper.getEpsilon(problem)}));
    } else {
      archive = new NondominatedPopulation();
    }

    return new RandomSearch(problem, generator, archive);
  }
  /**
   * Returns a new {@link RVEA} instance.
   *
   * @param properties the properties for customizing the new {@code RVEA} instance
   * @param problem the problem
   * @return a new {@code RVEA} instance
   */
  private Algorithm newRVEA(TypedProperties properties, Problem problem) {
    int divisionsOuter = 4;
    int divisionsInner = 0;

    if (problem.getNumberOfObjectives() < 2) {
      throw new FrameworkException("RVEA requires at least two objectives");
    }

    if (properties.contains("divisionsOuter") && properties.contains("divisionsInner")) {
      divisionsOuter = (int) properties.getDouble("divisionsOuter", 4);
      divisionsInner = (int) properties.getDouble("divisionsInner", 0);
    } else if (properties.contains("divisions")) {
      divisionsOuter = (int) properties.getDouble("divisions", 4);
    } else if (problem.getNumberOfObjectives() == 1) {
      divisionsOuter = 100;
    } else if (problem.getNumberOfObjectives() == 2) {
      divisionsOuter = 99;
    } else if (problem.getNumberOfObjectives() == 3) {
      divisionsOuter = 12;
    } else if (problem.getNumberOfObjectives() == 4) {
      divisionsOuter = 8;
    } else if (problem.getNumberOfObjectives() == 5) {
      divisionsOuter = 6;
    } else if (problem.getNumberOfObjectives() == 6) {
      divisionsOuter = 4;
      divisionsInner = 1;
    } else if (problem.getNumberOfObjectives() == 7) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 8) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 9) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 10) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else {
      divisionsOuter = 2;
      divisionsInner = 1;
    }

    // compute number of reference vectors
    int populationSize =
        (int)
            (CombinatoricsUtils.binomialCoefficient(
                    problem.getNumberOfObjectives() + divisionsOuter - 1, divisionsOuter)
                + (divisionsInner == 0
                    ? 0
                    : CombinatoricsUtils.binomialCoefficient(
                        problem.getNumberOfObjectives() + divisionsInner - 1, divisionsInner)));

    Initialization initialization = new RandomInitialization(problem, populationSize);

    ReferenceVectorGuidedPopulation population =
        new ReferenceVectorGuidedPopulation(
            problem.getNumberOfObjectives(),
            divisionsOuter,
            divisionsInner,
            properties.getDouble("alpha", 2.0));

    if (!properties.contains("sbx.swap")) {
      properties.setBoolean("sbx.swap", false);
    }

    if (!properties.contains("sbx.distributionIndex")) {
      properties.setDouble("sbx.distributionIndex", 30.0);
    }

    if (!properties.contains("pm.distributionIndex")) {
      properties.setDouble("pm.distributionIndex", 20.0);
    }

    Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);

    int maxGenerations = (int) (properties.getDouble("maxEvaluations", 10000) / populationSize);
    int adaptFrequency = (int) properties.getDouble("adaptFrequency", maxGenerations / 10);

    return new RVEA(problem, population, variation, initialization, maxGenerations, adaptFrequency);
  }
  /**
   * Returns a new {@link NSGAIII} instance.
   *
   * @param properties the properties for customizing the new {@code NSGAIII} instance
   * @param problem the problem
   * @return a new {@code NSGAIII} instance
   */
  private Algorithm newNSGAIII(TypedProperties properties, Problem problem) {
    int divisionsOuter = 4;
    int divisionsInner = 0;

    if (properties.contains("divisionsOuter") && properties.contains("divisionsInner")) {
      divisionsOuter = (int) properties.getDouble("divisionsOuter", 4);
      divisionsInner = (int) properties.getDouble("divisionsInner", 0);
    } else if (properties.contains("divisions")) {
      divisionsOuter = (int) properties.getDouble("divisions", 4);
    } else if (problem.getNumberOfObjectives() == 1) {
      divisionsOuter = 100;
    } else if (problem.getNumberOfObjectives() == 2) {
      divisionsOuter = 99;
    } else if (problem.getNumberOfObjectives() == 3) {
      divisionsOuter = 12;
    } else if (problem.getNumberOfObjectives() == 4) {
      divisionsOuter = 8;
    } else if (problem.getNumberOfObjectives() == 5) {
      divisionsOuter = 6;
    } else if (problem.getNumberOfObjectives() == 6) {
      divisionsOuter = 4;
      divisionsInner = 1;
    } else if (problem.getNumberOfObjectives() == 7) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 8) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 9) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else if (problem.getNumberOfObjectives() == 10) {
      divisionsOuter = 3;
      divisionsInner = 2;
    } else {
      divisionsOuter = 2;
      divisionsInner = 1;
    }

    int populationSize;

    if (properties.contains("populationSize")) {
      populationSize = (int) properties.getDouble("populationSize", 100);
    } else {
      // compute number of reference points
      populationSize =
          (int)
              (CombinatoricsUtils.binomialCoefficient(
                      problem.getNumberOfObjectives() + divisionsOuter - 1, divisionsOuter)
                  + (divisionsInner == 0
                      ? 0
                      : CombinatoricsUtils.binomialCoefficient(
                          problem.getNumberOfObjectives() + divisionsInner - 1, divisionsInner)));

      // round up to a multiple of 4
      populationSize = (int) Math.ceil(populationSize / 4d) * 4;
    }

    Initialization initialization = new RandomInitialization(problem, populationSize);

    ReferencePointNondominatedSortingPopulation population =
        new ReferencePointNondominatedSortingPopulation(
            problem.getNumberOfObjectives(), divisionsOuter, divisionsInner);

    Selection selection = null;

    if (problem.getNumberOfConstraints() == 0) {
      selection =
          new Selection() {

            @Override
            public Solution[] select(int arity, Population population) {
              Solution[] result = new Solution[arity];

              for (int i = 0; i < arity; i++) {
                result[i] = population.get(PRNG.nextInt(population.size()));
              }

              return result;
            }
          };
    } else {
      selection =
          new TournamentSelection(
              2,
              new ChainedComparator(
                  new AggregateConstraintComparator(),
                  new DominanceComparator() {

                    @Override
                    public int compare(Solution solution1, Solution solution2) {
                      return PRNG.nextBoolean() ? -1 : 1;
                    }
                  }));
    }

    // disable swapping variables in SBX operator to remain consistent with
    // Deb's implementation (thanks to Haitham Seada for identifying this
    // discrepancy)
    if (!properties.contains("sbx.swap")) {
      properties.setBoolean("sbx.swap", false);
    }

    if (!properties.contains("sbx.distributionIndex")) {
      properties.setDouble("sbx.distributionIndex", 30.0);
    }

    if (!properties.contains("pm.distributionIndex")) {
      properties.setDouble("pm.distributionIndex", 20.0);
    }

    Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);

    return new NSGAII(problem, population, null, selection, variation, initialization);
  }