Knapsack.java

/*
 * Java Genetic Algorithm Library (@__identifier__@).
 * Copyright (c) @__year__@ Franz Wilhelmstötter
 *
 * 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.
 *
 * Author:
 *    Franz Wilhelmstötter (franz.wilhelmstoetter@gmx.at)
 */
package org.jenetics.example;

import static org.jenetics.engine.EvolutionResult.toBestPhenotype;
import static org.jenetics.engine.limit.bySteadyFitness;

import java.util.Random;
import java.util.function.Function;
import java.util.stream.Collector;
import java.util.stream.Stream;

import org.jenetics.BitChromosome;
import org.jenetics.BitGene;
import org.jenetics.Genotype;
import org.jenetics.Mutator;
import org.jenetics.Phenotype;
import org.jenetics.RouletteWheelSelector;
import org.jenetics.SinglePointCrossover;
import org.jenetics.TournamentSelector;
import org.jenetics.engine.Engine;
import org.jenetics.engine.EvolutionStatistics;
import org.jenetics.util.RandomRegistry;

// This class represents a knapsack item, with a specific
// "size" and "value".
final class Item {
	public final double size;
	public final double value;

	Item(final double size, final double value) {
		this.size = size;
		this.value = value;
	}

	// Create a new random knapsack item.
	static Item random() {
		final Random r = RandomRegistry.getRandom();
		return new Item(r.nextDouble()*100, r.nextDouble()*100);
	}

	// Create a new collector for summing up the knapsack items.
	static Collector<Item, ?, Item> toSum() {
		return Collector.of(
			() -> new double[2],
			(a, b) -> {a[0] += b.size; a[1] += b.value;},
			(a, b) -> {a[0] += b[0]; a[1] += b[1]; return a;},
			r -> new Item(r[0], r[1])
		);
	}
}

// The knapsack fitness function class, which is parametrized with
// the available items and the size of the knapsack.
final class FF
	implements Function<Genotype<BitGene>, Double>
{
	private final Item[] items;
	private final double size;

	public FF(final Item[] items, final double size) {
		this.items = items;
		this.size = size;
	}

	@Override
	public Double apply(final Genotype<BitGene> gt) {
		final Item sum = ((BitChromosome)gt.getChromosome()).ones()
			.mapToObj(i -> items[i])
			.collect(Item.toSum());

		return sum.size <= this.size ? sum.value : 0;
	}
}

// The main class.
public class Knapsack {

	public static void main(final String[] args) {
		final int nitems = 15;
		final double kssize = nitems*100.0/3.0;

		final FF ff = new FF(
			Stream.generate(Item::random)
				.limit(nitems)
				.toArray(Item[]::new),
			kssize
		);

		// Configure and build the evolution engine.
		final Engine<BitGene, Double> engine = Engine
			.builder(ff, BitChromosome.of(nitems, 0.5))
			.populationSize(500)
			.survivorsSelector(new TournamentSelector<>(5))
			.offspringSelector(new RouletteWheelSelector<>())
			.alterers(
				new Mutator<>(0.115),
				new SinglePointCrossover<>(0.16))
			.build();

		// Create evolution statistics consumer.
		final EvolutionStatistics<Double, ?>
			statistics = EvolutionStatistics.ofNumber();

		final Phenotype<BitGene, Double> best = engine.stream()
			// Truncate the evolution stream after 7 "steady"
			// generations.
			.limit(bySteadyFitness(7))
			// The evolution will stop after maximal 100
			// generations.
			.limit(100)
			// Update the evaluation statistics after
			// each generation
			.peek(statistics)
			// Collect (reduce) the evolution stream to
			// its best phenotype.
			.collect(toBestPhenotype());

		System.out.println(statistics);
		System.out.println(best);
	}
}