/* * 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); } }