source: java/ecj/cecj/eval/SimpleCoevolutionaryEvaluator.java @ 1305

/*
  Copyright 2009 by Marcin Szubert
  Licensed under the Academic Free License version 3.0
*/

package cecj.eval;

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

import cecj.fitness.FitnessAggregateMethod;
import cecj.interaction.InteractionResult;
import cecj.interaction.InteractionScheme;
import cecj.sampling.SamplingMethod;
import cecj.statistics.CoevolutionaryStatistics;

import ec.EvolutionState;
import ec.Individual;
import ec.util.Parameter;

/**
 * 
 * Simple coevolutionary evaluator without any additional mechanisms.
 * 
 * This is the simplest implementation of conventional coevolutionary evaluation where interactions
 * between individuals can be performed in an arbitrary order. However, the character and the scope
 * of interactions can be different Ð it is defined by instantiating appropriate
 * <code>InteractionScheme</code> subclass. The evaluation proceeds as follows. First of all, a
 * reference set of opponent individuals is selected from each subpopulation. This task is handled
 * by a <code>SamplingMethod</code> realization. Distinct sampling methods can be used by different
 * subpopulations. Next, each subpopulation individuals are confronted with previously selected
 * opponents from subpopulations pointed by the concrete <code>InteractionScheme</code> class.
 * Finally, <code>FitnessAggregateMethod</code> is responsible for aggregating outcomes of these
 * confrontations into a single fitness measure which is used later during selection stage of the
 * evolutionary process. It evaluates individuals according to the outcomes of its interactions with
 * other individuals. Interactions are not restricted to intraspecific or interspecific type, i.e.
 * opponents can be chosen from the same population or any other coevolving population.
 * 
 * In contrast to <code>TournamentCoevolutionaryEvaluator</code> all interactions can be simulated
 * in any order. There are no sequential dependencies between interactions.
 * 
 * @author Marcin Szubert
 * 
 */
public class SimpleCoevolutionaryEvaluator extends CoevolutionaryEvaluator {

        protected static final String P_SUBPOP = "subpop";
        private static final String P_STATISTICS = "statistics";
        private static final String P_FITNESS_METHOD = "fitness-method";
        private static final String P_POP_INDS_WEIGHT = "pop-inds-weight";
        private static final String P_SAMPLING_METHOD = "sampling-method";
        private static final String P_INTERACTION_SCHEME = "interaction-scheme";

        /**
         * Tests used to interact with candidate solutions.
         */
        protected List<List<Individual>> opponents;

        /**
         * Methods of sampling the opponents from particular populations.
         */
        protected SamplingMethod[] samplingMethod;

        /**
         * The Method of aggregating multiple interaction outcomes into single value.
         */
        protected FitnessAggregateMethod[] fitnessAggregateMethod;

        /**
         * Specifies how interactions between populations look like.
         */
        protected InteractionScheme interactionScheme;

        /**
         * Gathers statistics about evaluation stage.
         */
        protected CoevolutionaryStatistics statistics;

        /**
         * Indicates how important are population opponents with respect to potential archival
         * opponents.
         */
        private int popIndsWeight;

        @Override
        public void setup(final EvolutionState state, final Parameter base) {
                super.setup(state, base);

                Parameter interactionSchemeParam = base.push(P_INTERACTION_SCHEME);
                interactionScheme = (InteractionScheme) (state.parameters.getInstanceForParameter(
                                interactionSchemeParam, null, InteractionScheme.class));
                interactionScheme.setup(state, interactionSchemeParam);

                Parameter popIndsWeightParam = base.push(P_POP_INDS_WEIGHT);
                popIndsWeight = state.parameters.getIntWithDefault(popIndsWeightParam, null, 1);

                Parameter statisticsParam = base.push(P_STATISTICS);
                if (state.parameters.exists(statisticsParam)) {
                        statistics = (CoevolutionaryStatistics) (state.parameters.getInstanceForParameter(
                                        statisticsParam, null, CoevolutionaryStatistics.class));
                        statistics.setup(state, statisticsParam);
                }

                opponents = new ArrayList<List<Individual>>(numSubpopulations);
                samplingMethod = new SamplingMethod[numSubpopulations];
                fitnessAggregateMethod = new FitnessAggregateMethod[numSubpopulations];

                for (int subpop = 0; subpop < numSubpopulations; subpop++) {
                        opponents.add(new ArrayList<Individual>());
                        setupSubpopulation(state, base, subpop);
                }
        }

        /**
         * Sets up fitness aggregate methods and sampling method for the given subpopulation.
         * 
         * @param state
         *            the current evolutionary state
         * @param base
         *            the base parameter
         * @param subpop
         *            the subpopulation index
         */
        private void setupSubpopulation(EvolutionState state, Parameter base, int subpop) {
                Parameter samplingMethodParam = base.push(P_SUBPOP).push("" + subpop).push(
                                P_SAMPLING_METHOD);
                samplingMethod[subpop] = (SamplingMethod) (state.parameters.getInstanceForParameter(
                                samplingMethodParam, null, SamplingMethod.class));
                samplingMethod[subpop].setup(state, samplingMethodParam);

                Parameter fitnessMethodParam = base.push(P_SUBPOP).push("" + subpop).push(P_FITNESS_METHOD);
                fitnessAggregateMethod[subpop] = (FitnessAggregateMethod) (state.parameters
                                .getInstanceForParameter(fitnessMethodParam, null, FitnessAggregateMethod.class));
        }

        @Override
        public void evaluatePopulation(EvolutionState state) {
                for (int subpop = 0; subpop < numSubpopulations; subpop++) {
                        opponents.set(subpop, findOpponentsFromSubpopulation(state, subpop));
                }

                for (int subpop = 0; subpop < numSubpopulations; subpop++) {
                        List<List<InteractionResult>> subpopulationResults = interactionScheme
                                        .performInteractions(state, subpop, opponents);

                        fitnessAggregateMethod[subpop].prepareToAggregate(state, subpop);
                        fitnessAggregateMethod[subpop].addToAggregate(state, subpop, subpopulationResults,
                                        popIndsWeight);
                        fitnessAggregateMethod[subpop].assignFitness(state, subpop);

                        if (statistics != null) {
                                statistics.printInteractionResults(state, subpopulationResults, subpop);
                        }
                }
        }

        /**
         * Samples subpopulation to choose a reference set of individuals. Other individuals can be
         * evaluated on the basis of interactions with this reference set. It may happen that
         * individuals from the same subpopulation are tested int this way - it depends on
         * 
         * @param subpop
         *            the index of subpopulation
         * @return a list of individuals sampled from the given subpopulation
         */
        private List<Individual> findOpponentsFromSubpopulation(EvolutionState state, int subpop) {
                return samplingMethod[subpop].sample(state, state.population.subpops[subpop].individuals);
        }

        /**
         * Returns the interaction scheme used during the evaluation.
         * 
         * @return the interaction scheme used by this evaluator
         */
        public InteractionScheme getInteractionScheme() {
                return interactionScheme;
        }
}