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/**
 * Copyright (C) 2010-2018 Gordon Fraser, Andrea Arcuri and EvoSuite
 * contributors
 *
 * This file is part of EvoSuite.
 *
 * EvoSuite is free software: you can redistribute it and/or modify it
 * under the terms of the GNU Lesser General Public License as published
 * by the Free Software Foundation, either version 3.0 of the License, or
 * (at your option) any later version.
 *
 * EvoSuite is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
 * Lesser Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with EvoSuite. If not, see .
 */
package org.evosuite.ga.metaheuristics.mulambda;

import org.evosuite.ga.Chromosome;
import org.evosuite.ga.ChromosomeFactory;
import org.evosuite.ga.FitnessFunction;
import java.util.ArrayList;
import java.util.List;

/**
 * (Mu + Lambda) EA
 *
 * @author José Campos
 */
public class MuPlusLambdaEA extends AbstractMuLambda {

  private static final long serialVersionUID = -8685698059226067598L;

  public MuPlusLambdaEA(ChromosomeFactory factory, int mu, int lambda) {
    super(factory, mu, lambda);
  }

  /** {@inheritDoc} */
  @SuppressWarnings("unchecked")
  @Override
  protected void evolve() {

    List offsprings = new ArrayList(this.lambda);

    // create new offsprings by mutating current population
    for (int i = 0; i < this.mu; i++) {
      for (int j = 0; j < this.lambda / this.mu; j++) {
        T offspring = (T) this.population.get(i).clone();
        this.notifyMutation(offspring);

        do {
          offspring.mutate();
        } while (!offspring.isChanged());

        offsprings.add(offspring);
      }
    }

    // update fitness values of offsprings
    for (T offspring : offsprings) {
      for (FitnessFunction fitnessFunction : this.fitnessFunctions) {
        fitnessFunction.getFitness(offspring);
        this.notifyEvaluation(offspring);
      }
    }

    for (int i = 0; i < this.population.size(); i++) {
      T bestOffspring = this.population.get(i);

      boolean offspring_is_better = false;
      for (T offspring : offsprings) {
        if (isBetterOrEqual(offspring, bestOffspring)) {
          bestOffspring = offspring;
          offspring_is_better = true;
        }
      }

      if (offspring_is_better) {
        // replace individual with a better one
        this.population.set(i, bestOffspring);
        // to prevent a population with only equal and dominant
        // individuals, here the best offspring is remove so that
        // it cannot be chosen again. in case of 1+1 and 1+Lambda EA
        // this optimization has no effect.
        offsprings.remove(bestOffspring);
      }

      this.population.get(i).updateAge(this.currentIteration);
    }
    assert this.population.size() == this.mu;

    this.currentIteration++;
  }
}