/*
* 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.ga.tsp;
import com.heatonresearch.aifh.evolutionary.population.BasicPopulation;
import com.heatonresearch.aifh.evolutionary.population.Population;
import com.heatonresearch.aifh.evolutionary.species.BasicSpecies;
import com.heatonresearch.aifh.evolutionary.train.basic.BasicEA;
import com.heatonresearch.aifh.genetic.crossover.SpliceNoRepeat;
import com.heatonresearch.aifh.genetic.genome.IntegerArrayGenome;
import com.heatonresearch.aifh.genetic.genome.IntegerArrayGenomeFactory;
import com.heatonresearch.aifh.genetic.mutate.MutateShuffle;
import com.heatonresearch.aifh.learning.score.ScoreFunction;
/**
* Find the shortest path through several cities with a genetic algorithm (GA).
* This example shows how to use it to find a potential solution to the Traveling Salesman Problem (TSP).
*/
public class GeneticTSPExample {
/**
* The number of cities to visit.
*/
public static final int CITIES = 50;
/**
* The size of the population.
*/
public static final int POPULATION_SIZE = 1000;
/**
* The square size of the map.
*/
public static final int MAP_SIZE = 256;
/**
* The maximum number of iterations to allow to have the same score before giving up.
*/
public static final int MAX_SAME_SOLUTION = 50;
/**
* The genetic algorithm.
*/
private BasicEA genetic;
/**
* The cities to visit.
*/
private City cities[];
/**
* Place the cities in random locations.
*/
private void initCities() {
cities = new City[CITIES];
for (int i = 0; i < cities.length; i++) {
int xPos = (int) (Math.random() * MAP_SIZE);
int yPos = (int) (Math.random() * MAP_SIZE);
cities[i] = new City(xPos, yPos);
}
}
/**
* Generate a random path through cities.
*/
private IntegerArrayGenome randomGenome() {
IntegerArrayGenome result = new IntegerArrayGenome(cities.length);
final int organism[] = result.getData();
final boolean taken[] = new boolean[cities.length];
for (int i = 0; i < organism.length - 1; i++) {
int icandidate;
do {
icandidate = (int) (Math.random() * organism.length);
} while (taken[icandidate]);
organism[i] = icandidate;
taken[icandidate] = true;
if (i == organism.length - 2) {
icandidate = 0;
while (taken[icandidate]) {
icandidate++;
}
organism[i + 1] = icandidate;
}
}
return result;
}
/**
* Create an initial random population of random paths through the cities.
*
* @return The random population.
*/
private Population initPopulation() {
Population result = new BasicPopulation(POPULATION_SIZE, null);
BasicSpecies defaultSpecies = new BasicSpecies();
defaultSpecies.setPopulation(result);
for (int i = 0; i < POPULATION_SIZE; i++) {
final IntegerArrayGenome genome = randomGenome();
defaultSpecies.add(genome);
}
result.setGenomeFactory(new IntegerArrayGenomeFactory(cities.length));
result.getSpecies().add(defaultSpecies);
return result;
}
/**
* Display the cities in the final path.
*/
public void displaySolution(IntegerArrayGenome solution) {
boolean first = true;
int[] path = solution.getData();
for (final int aPath : path) {
if (!first)
System.out.print(">");
System.out.print("" + aPath);
first = false;
}
System.out.println();
}
/**
* Setup and solve the TSP.
*/
public void solve() {
StringBuilder builder = new StringBuilder();
initCities();
Population pop = initPopulation();
ScoreFunction score = new TSPScore(cities);
genetic = new BasicEA(pop, score);
genetic.addOperation(0.9, new SpliceNoRepeat(CITIES / 3));
genetic.addOperation(0.1, new MutateShuffle());
int sameSolutionCount = 0;
int iteration = 1;
double lastSolution = Double.MAX_VALUE;
while (sameSolutionCount < MAX_SAME_SOLUTION) {
genetic.iteration();
double thisSolution = genetic.getLastError();
builder.setLength(0);
builder.append("Iteration: ");
builder.append(iteration++);
builder.append(", Best Path Length = ");
builder.append(thisSolution);
System.out.println(builder.toString());
if (Math.abs(lastSolution - thisSolution) < 1.0) {
sameSolutionCount++;
} else {
sameSolutionCount = 0;
}
lastSolution = thisSolution;
}
System.out.println("Good solution found:");
IntegerArrayGenome best = (IntegerArrayGenome) genetic.getBestGenome();
displaySolution(best);
genetic.finishTraining();
}
/**
* Program entry point.
*
* @param args Not used.
*/
public static void main(String args[]) {
GeneticTSPExample solve = new GeneticTSPExample();
solve.solve();
}
}