package jcgp.modules.ea;

import java.util.HashMap;

import jcgp.JCGP.Resources;
import jcgp.modules.mutator.Mutator;
import jcgp.parameters.IntegerParameter;
import jcgp.parameters.Parameter;
import jcgp.population.Chromosome;
import jcgp.population.Population;

/**
 * (μ + λ) EA.
 * 
 * 
 * @author Eduardo Pedroni
 *
 */
public class StandardEA implements EvolutionaryAlgorithm {
	
	private Chromosome fittestChromosome;
	
	private IntegerParameter parents, offspring;
	private HashMap<String, Parameter> localParameters;
	
	public StandardEA() {		
		parents = new IntegerParameter(1, "Parents");
		offspring = new IntegerParameter(4, "Offspring");		
		
		localParameters = new HashMap<String, Parameter>();
		
		localParameters.put("mu", parents);
		localParameters.put("lambda", offspring);
	}

	@Override
	public void evolve(Population population, Mutator mutator, Resources parameters) {
		// select fittest chromosome
		int fittest = 0;
		
		for (int i = 1; i < (int) parameters.get("popSize"); i++) {
			if (population.getChromosome(i).getFitness() >= population.getChromosome(fittest).getFitness()) {
				fittest = i;
			}
		}
		fittestChromosome = population.getChromosome(fittest);
		population.setBestIndividual(fittest);
		// create copies of fittest chromosome, mutate them
		Chromosome fc = population.getChromosome(fittest);
		for (int i = 0; i < (int) parameters.get("popSize"); i++) {
			if (i != fittest) {
				population.getChromosome(i).copyConnections(fc);
				mutator.mutate(population.getChromosome(i), parameters);
			}
		}
	}

	@Override
	public Chromosome getFittestChromosome() {
		return fittestChromosome;
	}

	@Override
	public HashMap<String, Parameter> activate(Resources parameters) {
		parameters.setManagedParameter("popSize", true);
		
		return localParameters;
	}
}