Added GA.
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@789511 13f79535-47bb-0310-9956-ffa450edef68
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Phil Steitz
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<item name="3D Geometry" href="/userguide/geometry.html"/>
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<item name="Optimization" href="/userguide/optimization.html"/>
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<item name="Ordinary Differential Equations" href="/userguide/ode.html"/>
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<item name="Genetic Algorithms" href="/userguide/genetics.html"/>
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</menu>
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</body>
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@ -0,0 +1,136 @@
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<?xml version="1.0"?>
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<!--
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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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this work for additional information regarding copyright ownership.
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The ASF licenses this file to You under the Apache License, Version 2.0
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(the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
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<!-- $Revision:$ $Date:$ -->
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<document url="genetics.html">
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<properties>
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<title>The Commons Math User Guide - Genetic Algorithms</title>
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</properties>
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<body>
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<section name="14 Genetic Algorithms">
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<subsection name="14.1 Overview" href="overview">
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<p>
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The genetics package provides a framework and implementations for
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genetic algorithms.
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</p>
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</subsection>
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<subsection name="14.2 GA Framework">
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<p>
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<a href="../apidocs/org/apache/commons/math/genetics/GeneticAlgorithm.html">
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org.apache.commons.math.genetic.GeneticAlgorithm</a> provides an
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execution framework for Genetic Algorithms (GA).
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<a href="../apidocs/org/apache/commons/math/genetics/Population.html">Populations,</a> consisting
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of <a href="../apidocs/org/apache/commons/math/genetics/Chromosome.html">
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Chromosomes</a> are evolved by the <code>GeneticAlgorithm</code> until a
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<a href="../apidocs/org/apache/commons/math/genetics/StoppingCondition.html">StoppingCondition</a>
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is reached. Evolution is determined by
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<a href="../apidocs/org/apache/commons/math/genetics/SelectionPolicy.html">SelectionPolicies</a>,
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<a href="../apidocs/org/apache/commons/math/genetics/MutationPolicy.html"> MutationPolicies</a>
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and <a href="../apidocs/org/apache/commons/math/genetics/Fitness.html">Fitness</a>.
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</p>
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<p>
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The GA itself is implemented by the <code>evolve</code> method of the <code>GeneticAlgorithm</code> class,
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which looks like this:
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<source>
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public Population evolve(Population initial, StoppingCondition condition) {
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Population current = initial;
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while (!condition.isSatisfied(current)) {
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current = nextGeneration(current);
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}
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return current;
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}
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</source>
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The <code>nextGeneration</code> method implements the following algorithm:
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<ol>
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<li>Get nextGeneration population to fill from <code>current</code>
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generation, using its nextGeneration method</li>
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<li>Loop until new generation is filled:</li>
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<ul><li>Apply configured <code>SelectionPolicy</code> to select a pair of parents
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from <code>current</code></li>
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<li>With probability =
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<a href="../apidocs/org/apache/commons/math/genetics/GeneticAlgorithm.html#getCrossoverRate()">
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getCrossoverRate()</a>, apply configured <code>CrossoverPolicy</code> to parents</li>
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<li>With probability =
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<a href="../apidocs/org/apache/commons/math/genetics/GeneticAlgorithm.html#getMutationRate()">
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getMutationRate()</a>,
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apply configured <code>MutationPolicy</code> to each of the offspring</li>
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<li>Add offspring individually to nextGeneration,
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space permitting</li>
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</ul>
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<li>Return nextGeneration</li>
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</ol>
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</p>
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</subsection>
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<subsection name="14.3 Implementation">
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<p>
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Here is an example GA execution:
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<source>
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// initialize a new genetic algorithm
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GeneticAlgorithm ga = new GeneticAlgorithm(
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new OnePointCrossover<Integer>(),
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1,
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new RandomKeyMutation(),
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0.10,
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new TournamentSelection(TOURNAMENT_ARITY)
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);
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// initial population
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Population initial = getInitialPopulation();
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// stopping condition
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StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);
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// run the algorithm
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Population finalPopulation = ga.evolve(initial, stopCond);
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// best chromosome from the final population
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Chromosome bestFinal = finalPopulation.getFittestChromosome();
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</source>
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The arguments to the <code>GeneticAlgorithm</code> constructor above are: <br/>
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<table>
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<tr><th>Parameter</th><th>value in example</th><th>meaning</th></tr>
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<tr><td>crossoverPolicy</td>
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<td><a href="../apidocs/org/apache/commons/math/genetics/OnePointCrossover.html">OnePointCrossover</a></td>
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<td>A random crossover point is selected and the first part from each parent is copied to the corresponding
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child, and the second parts are copied crosswise.</td></tr>
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<tr><td>crossoverRate</td>
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<td>1</td>
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<td>Always apply crossover</td></tr>
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<tr><td>mutationPolicy</td>
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<td><a href="../apidocs/org/apache/commons/math/genetics/RandomKeyMutation.html">RandomKeyMutation</a></td>
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<td>Changes a randomly chosen element of the array representation to a random value uniformly distributed in [0,1].</td></tr>
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<tr><td>mutationRate</td>
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<td>.1</td>
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<td>Apply mutation with probability 0.1 - that is, 10% of the time.</td></tr>
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<tr><td>selectionPolicy</td>
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<td><a href="../apidocs/org/apache/commons/math/genetics/TournamentSelection.html">TournamentSelection</a></td>
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<td>Each of the two selected chromosomes is selected based on an n-ary tournament -- this is done by drawing
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n random chromosomes without replacement from the population, and then selecting the fittest chromosome among them.</td></tr>
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</table><br/>
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The algorithm starts with an <code>initial</code> population of <code>Chromosomes.</code> and executes until
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the specified <a href="../apidocs/org/apache/commons/math/genetics/StoppingCondition.html">StoppingCondition</a>
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is reached. In the example above, a
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<a href="../apidocs/org/apache/commons/math/genetics/FixedGenerationCount.html">FixedGenerationCount</a>
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stopping condition is used, which means the algorithm proceeds through a fixed number of generations.
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</p>
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</subsection>
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</section>
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</body>
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</document>
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@ -131,7 +131,13 @@
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<li><a href="ode.html#a13.2_Discrete_Events_Handling">13.2 Discrete Events Handling</a></li>
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<li><a href="ode.html#a13.3_ODE_Problems">13.3 ODE Problems</a></li>
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<li><a href="ode.html#a13.4_Integrators">13.4 Integrators</a></li>
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</ul></li>
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</ul></li>
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<li><a href="genetics.html">14. Genetic Algorithms</a>
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<ul>
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<li><a href="genetics.html#a14.1_Overview">14.1 Overview</a></li>
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<li><a href="genetics.html#a14.2_GA_Framework">14.2 GA Framework</a></li>
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<li><a href="genetics.html#a14.3_Implementation">14.3 Implementation and Examples</a></li>
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</ul></li>
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</ul>
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</section>
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