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package org.evosuite.ga.metaheuristics;

import org.evosuite.Properties;
import org.evosuite.ga.Chromosome;
import org.evosuite.ga.ChromosomeFactory;
import org.evosuite.ga.ConstructionFailedException;
import org.evosuite.runtime.util.AtMostOnceLogger;
import org.evosuite.utils.Randomness;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.ArrayList;
import java.util.List;

/**
 * Implementation of a BreederGA as described in:
 * H. Muëhlenbein and D. Schlierkamp-Voosen,
 * "Predictive models for the breeder genetic algorithm. contiunous parameter optimization,”
 * Evolutionary Computation, vol. 1, no. 1, pp. 25–49, 1993.
 *
 * This uses standard mutation and crossover.
 *
 * @param 
 */
public class BreederGA extends StandardGA {

    private final Logger logger = LoggerFactory.getLogger(BreederGA.class);

    /**
     * Constructor
     *
     * @param factory
     *            a {@link org.evosuite.ga.ChromosomeFactory} object.
     */
    public BreederGA(ChromosomeFactory factory) {
        super(factory);
    }

    @Override
    protected void evolve() {
        List newGeneration = new ArrayList<>();

        // Elitism
        newGeneration.addAll(elitism());

        // Truncation selection
        List candidates = population.subList(0, (int)(population.size() * Properties.TRUNCATION_RATE));

        // If there are no candidates, the parameters are not set optimally,
        if(candidates.size() <= 1) {
            candidates.addAll(population);
            AtMostOnceLogger.warn(logger, "Not sufficient candidates for reproduction, consider increasing the population size, or the truncation rate");
        }

        // new_generation.size() < population_size
        while (!isNextPopulationFull(newGeneration)) {

            T parent1 = Randomness.choice(candidates);
            T parent2 = Randomness.choice(candidates);

            // Self-breeding is nor allowed
            if(parent1 == parent2) {
                continue;
            }

            T offspring1 = (T)parent1.clone();
            T offspring2 = (T)parent2.clone();

            try {
                crossoverFunction.crossOver(offspring1, offspring2);
            } catch (ConstructionFailedException e) {
                logger.info("CrossOver/Mutation failed.");
                continue;
            }


            T offspring = Randomness.choice(offspring1, offspring2);

            notifyMutation(offspring);
            offspring.mutate();

            if(offspring.isChanged()) {
                offspring.updateAge(currentIteration);
            }
            if (!isTooLong(offspring)) {
                newGeneration.add(offspring1);
            }
        }

        population = newGeneration;
        //archive
        updateFitnessFunctionsAndValues();
        //
        currentIteration++;
    }

}