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ECJ, A Java-based Evolutionary Computation Research System. ECJ is a research EC system written in Java. It was designed to be highly flexible, with nearly all classes (and all of their settings) dynamically determined at runtime by a user-provided parameter file. All structures in the system are arranged to be easily modifiable. Even so, the system was designed with an eye toward efficiency. ECJ is developed at George Mason University's ECLab Evolutionary Computation Laboratory. The software has nothing to do with its initials' namesake, Evolutionary Computation Journal. ECJ's sister project is MASON, a multi-agent simulation system which dovetails with ECJ nicely.

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/*
  Copyright 2006 by Sean Luke
  Licensed under the Academic Free License version 3.0
  See the file "LICENSE" for more information
*/


/*
 * Created on Apr 16, 2005 12:36:14 PM
 * 
 * By: spaus, Hovden
 */
package ec.app.gui;

import ec.display.chart.*;
import ec.EvolutionState;
import ec.Fitness;
import ec.util.Parameter;
import ec.vector.*;

/**
 * @author spaus
 */
public class SimpleBarChartStatistics extends BarChartStatistics 
    { //Hovden

    private int[] seriesID;
    
    public void setup(EvolutionState state, Parameter base) {
        super.setup(state, base);
        int numSubPops = state.parameters.getInt(new Parameter("pop.subpops"),null);
        
        seriesID = new int[numSubPops];
        
        for (int i = 0; i < numSubPops; ++i) {
            seriesID[i] = i; //series id identifies each sub pop
            }
        }
    
    public void postEvaluationStatistics(EvolutionState state) {
        super.postEvaluationStatistics(state);
        
        for (int subPop = 0; subPop < state.population.subpops.length; ++subPop) {
            Fitness bestFit = state.population.subpops[subPop].individuals[0].fitness;
            for (int i = 1; i < state.population.subpops[subPop].individuals.length; ++i) {
                Fitness fit = state.population.subpops[subPop].individuals[i].fitness;
                if (fit.betterThan(bestFit))
                    bestFit = fit;
                
                //Best individual is found, make a bar graph
                makeBar(seriesID[subPop], ((DoubleVectorIndividual)state.population.subpops[subPop].individuals[i]).genome);
                }
            }
        }
    }