/*
* 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.examples.operations;
import com.heatonresearch.aifh.evolutionary.population.BasicPopulation;
import com.heatonresearch.aifh.evolutionary.population.Population;
import com.heatonresearch.aifh.evolutionary.train.EvolutionaryAlgorithm;
import com.heatonresearch.aifh.evolutionary.train.basic.BasicEA;
import com.heatonresearch.aifh.genetic.genome.DoubleArrayGenome;
import com.heatonresearch.aifh.genetic.genome.DoubleArrayGenomeFactory;
import com.heatonresearch.aifh.genetic.genome.IntegerArrayGenome;
import com.heatonresearch.aifh.genetic.genome.IntegerArrayGenomeFactory;
import com.heatonresearch.aifh.genetic.mutate.MutatePerturb;
import com.heatonresearch.aifh.genetic.mutate.MutateShuffle;
import com.heatonresearch.aifh.learning.MLMethod;
import com.heatonresearch.aifh.learning.score.ScoreFunction;
import com.heatonresearch.aifh.randomize.GenerateRandom;
import com.heatonresearch.aifh.randomize.MersenneTwisterGenerateRandom;
import java.util.Arrays;
/**
* This example shows how two different mutate operators create an offspring from a genome.
* <p/>
* Sample output from this example:
* <p/>
* Mutate shuffle
* Parent: [1, 2, 3, 4, 5]
* Offspring: [1, 3, 2, 4, 5]
* Mutate peterb
* Parent: [1.0, 2.0, 3.0, 4.0, 5.0]
* Offspring: [0.9684564148017776, 2.0231188741090955, 3.200690276405833, 4.050125858385886, 4.531099177190473]
*/
public class MutateExample {
/**
* Demonstrate the mutate shuffle operator. An offspring will be created by swapping two
* individual genes.
*/
public static void mutateShuffle() {
System.out.println("Mutate shuffle");
// Create a random number generator
GenerateRandom rnd = new MersenneTwisterGenerateRandom();
// Create a new population.
Population pop = new BasicPopulation();
pop.setGenomeFactory(new IntegerArrayGenomeFactory(5));
// Create a trainer with a very simple score function. We do not care
// about the calculation of the score, as they will never be calculated.
EvolutionaryAlgorithm train = new BasicEA(pop, new ScoreFunction() {
@Override
public double calculateScore(MLMethod method) {
return 0;
}
@Override
public boolean shouldMinimize() {
return false;
}
});
// Create a shuffle operator. Use it 1.0 (100%) of the time.
MutateShuffle opp = new MutateShuffle();
train.addOperation(1.0, opp);
// Create a single parent, the genes are set to 1,2,3,4,5.
IntegerArrayGenome[] parents = new IntegerArrayGenome[1];
parents[0] = (IntegerArrayGenome) pop.getGenomeFactory().factor();
for (int i = 1; i <= 5; i++) {
parents[0].getData()[i - 1] = i;
}
// Create an array to hold the offspring.
IntegerArrayGenome[] offspring = new IntegerArrayGenome[1];
offspring[0] = new IntegerArrayGenome(5);
// Perform the operation
opp.performOperation(rnd, parents, 0, offspring, 0);
// Display the results
System.out.println("Parent: " + Arrays.toString(parents[0].getData()));
System.out.println("Offspring: " + Arrays.toString(offspring[0].getData()));
}
/**
* Demonstrate the mutate peterb operator. An offspring will be created by randomly changing each
* gene.
*/
public static void mutatePeterb() {
System.out.println("Mutate Perturb");
GenerateRandom rnd = new MersenneTwisterGenerateRandom();
// Create a new population.
Population pop = new BasicPopulation();
pop.setGenomeFactory(new DoubleArrayGenomeFactory(5));
// Create a trainer with a very simple score function. We do not care
// about the calculation of the score, as they will never be calculated.
EvolutionaryAlgorithm train = new BasicEA(pop, new ScoreFunction() {
@Override
public double calculateScore(MLMethod method) {
return 0;
}
@Override
public boolean shouldMinimize() {
return false;
}
});
MutatePerturb opp = new MutatePerturb(0.1);
train.addOperation(1.0, opp);
// Create a peterb operator. Use it 1.0 (100%) of the time.
DoubleArrayGenome[] parents = new DoubleArrayGenome[1];
parents[0] = (DoubleArrayGenome) pop.getGenomeFactory().factor();
parents[0].setPopulation(pop);
for (int i = 1; i <= 5; i++) {
parents[0].getData()[i - 1] = i;
}
// Create an array to hold the offspring.
DoubleArrayGenome[] offspring = new DoubleArrayGenome[1];
offspring[0] = new DoubleArrayGenome(5);
// Perform the operation
opp.performOperation(rnd, parents, 0, offspring, 0);
// Display the results
System.out.println("Parent: " + Arrays.toString(parents[0].getData()));
System.out.println("Offspring: " + Arrays.toString(offspring[0].getData()));
}
/**
* Main entry point for the program.
*
* @param args Not used.
*/
public static void main(String[] args) {
mutateShuffle();
mutatePeterb();
}
}