/*
* Artificial Intelligence for Humans
* Volume 2: Nature Inspired Algorithms
* Java Version
* http://www.aifh.org
* http://www.jeffheaton.com
*
* Code repository:
* https://github.com/jeffheaton/aifh
*
* Copyright 2014 by Jeff Heaton
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package com.heatonresearch.aifh.genetic.mutate;
import com.heatonresearch.aifh.evolutionary.genome.Genome;
import com.heatonresearch.aifh.evolutionary.opp.EvolutionaryOperator;
import com.heatonresearch.aifh.evolutionary.train.EvolutionaryAlgorithm;
import com.heatonresearch.aifh.genetic.genome.ArrayGenome;
import com.heatonresearch.aifh.randomize.GenerateRandom;
/**
* A simple mutation where genes are shuffled. This mutation will not produce
* repeated genes.
*/
public class MutateShuffle implements EvolutionaryOperator {
/**
* The owner.
*/
private EvolutionaryAlgorithm owner;
/**
* {@inheritDoc}
*/
@Override
public void init(final EvolutionaryAlgorithm theOwner) {
this.owner = theOwner;
}
/**
* @return The number of offspring produced, which is 1 for this mutation.
*/
@Override
public int offspringProduced() {
return 1;
}
/**
* {@inheritDoc}
*/
@Override
public int parentsNeeded() {
return 1;
}
/**
* {@inheritDoc}
*/
@Override
public void performOperation(final GenerateRandom rnd, final Genome[] parents,
final int parentIndex, final Genome[] offspring,
final int offspringIndex) {
final ArrayGenome parent = (ArrayGenome) parents[parentIndex];
offspring[offspringIndex] = this.owner.getPopulation()
.getGenomeFactory().factor();
final ArrayGenome child = (ArrayGenome) offspring[offspringIndex];
child.copy(parent);
final int length = parent.size();
int iswap1 = (int) (rnd.nextDouble() * length);
int iswap2 = (int) (rnd.nextDouble() * length);
// can't be equal
if (iswap1 == iswap2) {
// move to the next, but
// don't go out of bounds
if (iswap1 > 0) {
iswap1--;
} else {
iswap1++;
}
}
// make sure they are in the right order
if (iswap1 > iswap2) {
final int temp = iswap1;
iswap1 = iswap2;
iswap2 = temp;
}
child.swap(iswap1, iswap2);
}
}