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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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/*
  Portions copyright 2010 by Sean Luke, Robert Hubley, and George Mason University
  Licensed under the Academic Free License version 3.0
  See the file "LICENSE" for more information
*/

package ec.multiobjective.spea2;

import java.io.*;
import ec.util.*;
import ec.multiobjective.*;
import ec.*;

/* 
 * SPEA2MultiObjectiveFitness.java
 * 
 * Created: Sat Oct 16 11:24:43 EDT 2010
 * By: Sean Luke
 * Replaces earlier class by: Robert Hubley, with revisions by Gabriel Balan and Keith Sullivan
 */

/**
 * SPEA2MultiObjectiveFitness is a subclass of MultiObjectiveFitness which adds three auxiliary fitness
 * measures used in SPEA2: strength S(i), kthNNDistance D(i), and a final fitness value R(i) + D(i).  
 * Note that so-called "raw fitness" (what Sean calls "Wimpiness" in Essentials of Metaheuristics) is 
 * not retained.
 * 
 * The fitness comparison operators solely use the 'fitness' value R(i) + D(i).
 */

public class SPEA2MultiObjectiveFitness extends MultiObjectiveFitness
    {
    public static final String SPEA2_FITNESS_PREAMBLE = "Fitness: ";
    public static final String SPEA2_STRENGTH_PREAMBLE = "Strength: ";
    public static final String SPEA2_DISTANCE_PREAMBLE = "Distance: ";

    public String[] getAuxilliaryFitnessNames() { return new String[] { "Strength", "Raw Fitness", "Kth NN Distance" }; }
    public double[] getAuxilliaryFitnessValues() { return new double[] { strength, fitness, kthNNDistance }; }
        
    /** SPEA2 strength (# of nodes it dominates) */
    public double strength; // S(i)

    /** SPEA2 NN distance */
    public double kthNNDistance; // D(i)

    /** Final SPEA2 fitness.  Equals the raw fitness R(i) plus the kthNNDistance D(i). */
    public double fitness;

    public String fitnessToString()
        {
        return super.fitnessToString() + "\n" + SPEA2_FITNESS_PREAMBLE + Code.encode(fitness) + "\n" + SPEA2_STRENGTH_PREAMBLE + Code.encode(strength) + "\n" + SPEA2_DISTANCE_PREAMBLE + Code.encode(kthNNDistance);
        }

    public String fitnessToStringForHumans()
        {
        return super.fitnessToStringForHumans() + "\n" + SPEA2_STRENGTH_PREAMBLE + strength + "\n" + SPEA2_DISTANCE_PREAMBLE + kthNNDistance + " " + SPEA2_FITNESS_PREAMBLE + fitness;
        }

    public void readFitness(final EvolutionState state, final LineNumberReader reader) throws IOException
        {
        super.readFitness(state, reader);
        fitness = Code.readDoubleWithPreamble(SPEA2_FITNESS_PREAMBLE, state, reader);
        strength = Code.readDoubleWithPreamble(SPEA2_STRENGTH_PREAMBLE, state, reader);
        kthNNDistance = Code.readDoubleWithPreamble(SPEA2_DISTANCE_PREAMBLE, state, reader);
        }

    public void writeFitness(final EvolutionState state, final DataOutput dataOutput) throws IOException
        {
        super.writeFitness(state, dataOutput);
        dataOutput.writeDouble(fitness);
        dataOutput.writeDouble(strength);
        dataOutput.writeDouble(fitness);
        dataOutput.writeDouble(kthNNDistance);
        writeTrials(state, dataOutput);
        }

    public void readFitness(final EvolutionState state, final DataInput dataInput) throws IOException
        {
        super.readFitness(state, dataInput);
        fitness = dataInput.readDouble();
        strength = dataInput.readDouble();
        fitness = dataInput.readDouble();
        kthNNDistance = dataInput.readDouble();
        readTrials(state, dataInput);
        }

    /**
     * The selection criteria in SPEA2 uses the computed fitness, and not
     * pareto dominance.
     */
    public boolean equivalentTo(Fitness _fitness)
        {
        return fitness == ((SPEA2MultiObjectiveFitness)_fitness).fitness;
        }

    /**
     * The selection criteria in SPEA2 uses the computed fitness, and not
     * pareto dominance.
     */
    public boolean betterThan(Fitness _fitness)
        {
        return fitness < ((SPEA2MultiObjectiveFitness)_fitness).fitness;
        }
    }