#169: Implement (μ + λ) ES example.

org.jenetics.example/src/main/java/org/jenetics/example/MpLStrategy.java

@@ -0,0 +1,69 @@
+/*
+ * 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 org.jenetics.DoubleGene;
+import org.jenetics.Mutator;
+import org.jenetics.TruncationSelector;
+import org.jenetics.engine.Codec;
+import org.jenetics.engine.Engine;
+import org.jenetics.engine.EvolutionResult;
+import org.jenetics.engine.codecs;
+import org.jenetics.util.DoubleRange;
+
+/**
+ * The (μ + λ) evolution strategy is among the simplest ES algorithms.
+ *
+ * @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
+ * @version !__version__!
+ * @since !__version__!
+ */
+public class MpLStrategy {
+
+	static double fitness(final double x) {
+		return x;
+	}
+
+	public static void main(final String[] args) {
+		final int μ = 5;
+		final int λ = 20;
+		final double p = 0.2;
+
+		final Codec<Double, DoubleGene> codec = codecs
+			.ofScalar(DoubleRange.of(0, 1));
+
+		final Engine<DoubleGene, Double> engine = Engine
+			.builder(MpLStrategy::fitness, codec)
+			.populationSize(λ)
+			.survivorsSize(μ)
+			.selector(new TruncationSelector<>(μ))
+			.alterers(new Mutator<>(p))
+			.build();
+
+		System.out.println(
+			codec.decode(
+				engine.stream()
+					.limit(100)
+					.collect(EvolutionResult.toBestGenotype())
+			)
+		);
+	}
+
+}