<tr>
<td>
OnePointCrossover
<td>A random crossover point is selected and the first part from each parent is copied to the corresponding
child, and the second parts are copied crosswise.</td>
<tr>
<td>1
<td>Always apply crossover
<tr>
<td>
RandomKeyMutation
<td>Changes a randomly chosen element of the array representation to a random value uniformly distributed in [0,1].
<tr>
<td>.1
<td>Apply mutation with probability 0.1 - that is, 10% of the time.
<tr>
<td>
TournamentSelection
<td>Each of the two selected chromosomes is selected based on an n-ary tournament -- this is done by drawing
n random chromosomes without replacement from the population, and then selecting the fittest chromosome among them.</td>
</table>
The algorithm starts with an <code>initial population of
Chromosomes.
and executes until
the specified <a href="../apidocs/org/apache/commons/math3/genetics/StoppingCondition.html">StoppingCondition
is reached. In the example above, a
<a href="../apidocs/org/apache/commons/math3/genetics/FixedGenerationCount.html">FixedGenerationCount
stopping condition is used, which means the algorithm proceeds through a fixed number of generations.
</p>
</subsection>
</section>
</body>
</document>
Other Java examples (source code examples)
Here is a short list of links related to this Java genetics.xml source code file:
|
The genetics.xml Java example source code
<?xml version="1.0"?>
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<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
<document url="genetics.html">
<properties>
<title>The Commons Math User Guide - Genetic Algorithms
</properties>
<body>
<section name="16 Genetic Algorithms">
<subsection name="16.1 Overview" href="overview">
<p>
The genetics package provides a framework and implementations for
genetic algorithms.
</p>
</subsection>
<subsection name="16.2 GA Framework">
<p>
<a href="../apidocs/org/apache/commons/math3/genetics/GeneticAlgorithm.html">
GeneticAlgorithm</a> provides an execution framework for Genetic Algorithms (GA).
<a href="../apidocs/org/apache/commons/math3/genetics/Population.html">
Populations,</a> consisting of
Chromosomes</a> are evolved by the GeneticAlgorithm until a
<a href="../apidocs/org/apache/commons/math3/genetics/StoppingCondition.html">
StoppingCondition</a> is reached. Evolution is determined by
SelectionPolicy</a>,
MutationPolicy</a> and
Fitness</a>.
</p>
<p>
The GA itself is implemented by the <code>evolve method of the
<code>GeneticAlgorithm class,
which looks like this:
<source>public Population evolve(Population initial, StoppingCondition condition) {
Population current = initial;
while (!condition.isSatisfied(current)) {
current = nextGeneration(current);
}
return current;
}
</source>
The <code>nextGeneration method implements the following algorithm:
<ol>
<li>Get nextGeneration population to fill from current
generation, using its nextGeneration method</li>
<li>Loop until new generation is filled:
<ul>Apply configured SelectionPolicy to select a pair of parents
from <code>current
<li>With probability =
<a href="../apidocs/org/apache/commons/math3/genetics/GeneticAlgorithm.html#getCrossoverRate()">
getCrossoverRate()</a>, apply configured CrossoverPolicy to parents
<li>With probability =
<a href="../apidocs/org/apache/commons/math3/genetics/GeneticAlgorithm.html#getMutationRate()">
getMutationRate()</a>,
apply configured <code>MutationPolicy to each of the offspring
<li>Add offspring individually to nextGeneration,
space permitting</li>
</ul>
<li>Return nextGeneration
</ol>
</p>
</subsection>
<subsection name="16.3 Implementation">
<p>
Here is an example GA execution:
<source>
// initialize a new genetic algorithm
GeneticAlgorithm ga = new GeneticAlgorithm(
new OnePointCrossover<Integer>(),
1,
new RandomKeyMutation(),
0.10,
new TournamentSelection(TOURNAMENT_ARITY)
);
// initial population
Population initial = getInitialPopulation();
// stopping condition
StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);
// run the algorithm
Population finalPopulation = ga.evolve(initial, stopCond);
// best chromosome from the final population
Chromosome bestFinal = finalPopulation.getFittestChromosome();
</source>
The arguments to the <code>GeneticAlgorithm constructor above are:
<table>
<tr> | Parameter | value in example | meaning |
crossoverPolicy |
crossoverRate |
mutationPolicy |
mutationRate |
selectionPolicy |