package ec.de;

import ec.*;
import ec.util.*;
import ec.vector.*;

/* 
 * Rand1EitherOrDEBreeder.java
 * 
 * Created: Wed Apr 26 18:01:11 2006
 * By: Liviu Panait
 */

/**
 * Rand1EitherOrDEBreeder is a differential evolution breeding operator.
 * The code is derived from a DE algorithm, known as DE/rand/1/either-or, 
 * found on page 141 of
 * "Differential Evolution: A Practical Approach to Global Optimization"
 * by Kenneth Price, Rainer Storn, and Jouni Lampinen.
 *
 * 

Rand1EitherOrDEBreeder requires that all individuals be DoubleVectorIndividuals. * *

In short, the algorithm is as follows. For each individual in the population, we produce a child * by selecting three (different) individuals, none the original individual, called r0, r1, and r2. * We then create an individal c, defined either c = r0 + F * (r1 - r2), or as c = r0 + 0.5 * (F+1) * (r1 + r2 - 2 * r0), * depending on a coin flip of probability "PF" (if 'true', the first equation is used, else the second). * Unlike the other DEBreeders in this package, we do *not* cross over the child with the original individual. * In fact, if the crossover probability is specified, Rand1EitherOrDEBreeder will issue a warning that it's * not using it. * *

This class should be used in conjunction with * DEEvaluator, which allows the children to enter the population only if they're superior to their * parents (the original individuals). If so, they replace their parents. * *

Parameters
*

* * *
base.pf
* 0.0 <= double <= 1.0
The "PF" probability of mutation type
* * @author Liviu Panait and Sean Luke * @version 2.0 */ public class Rand1EitherOrDEBreeder extends DEBreeder { public double PF = 0.0; public static final String P_PF = "pf"; public void setup(final EvolutionState state, final Parameter base) { super.setup(state,base); PF = state.parameters.getDouble(base.push(P_PF),null,0.0); if ( PF < 0.0 || PF > 1.0 ) state.output.fatal( "Parameter not found, or its value is outside of [0.0,1.0].", base.push(P_PF), null ); if (state.parameters.exists(base.push(P_Cr), null)) state.output.warning("Crossover parameter specified, but Rand1EitherOrDEBreeder does not use crossover.", base.push(P_Cr)); } public DoubleVectorIndividual createIndividual( final EvolutionState state, int subpop, int index, int thread ) { Individual[] inds = state.population.subpops[subpop].individuals; DoubleVectorIndividual v = (DoubleVectorIndividual)(state.population.subpops[subpop].species.newIndividual(state, thread)); int retry = -1; do { retry++; // select three indexes different from each other and from that of the current parent int r0, r1, r2; do { r0 = state.random[thread].nextInt(inds.length); } while( r0 == index ); do { r1 = state.random[thread].nextInt(inds.length); } while( r1 == r0 || r1 == index ); do { r2 = state.random[thread].nextInt(inds.length); } while( r2 == r1 || r2 == r0 || r2 == index ); DoubleVectorIndividual g0 = (DoubleVectorIndividual)(inds[r0]); DoubleVectorIndividual g1 = (DoubleVectorIndividual)(inds[r1]); DoubleVectorIndividual g2 = (DoubleVectorIndividual)(inds[r2]); for(int i = 0; i < v.genome.length; i++) if (state.random[thread].nextBoolean(PF)) v.genome[i] = g0.genome[i] + F * (g1.genome[i] - g2.genome[i]); else v.genome[i] = g0.genome[i] + 0.5 * (F+1) * (g1.genome[i] + g2.genome[i] - 2 * g0.genome[i]); } while(!valid(v) && retry < retries); if (retry >= retries && !valid(v)) // we reached our maximum { // completely reset and be done with it v.reset(state, thread); } return v; // no crossover is performed } }

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