use of org.evosuite.ga.variables.DoubleVariable in project evosuite by EvoSuite.
the class TestBeales method testBeales.
/**
* Testing NSGA-II with Beales Problem
*
* @throws IOException
* @throws NumberFormatException
*/
@Test
public void testBeales() throws NumberFormatException, IOException {
Properties.MUTATION_RATE = 1d / 2d;
ChromosomeFactory<?> factory = new RandomFactory(false, 2, -4.5, 4.5);
// GeneticAlgorithm<?> ga = new NSGAII(factory);
GeneticAlgorithm<?> ga = new NSGAII(factory);
BinaryTournamentSelectionCrowdedComparison ts = new BinaryTournamentSelectionCrowdedComparison();
// BinaryTournament ts = new BinaryTournament();
ga.setSelectionFunction(ts);
ga.setCrossOverFunction(new SBXCrossover());
Problem p = new Beales();
final FitnessFunction f1 = (FitnessFunction) p.getFitnessFunctions().get(0);
ga.addFitnessFunction(f1);
// execute
ga.generateSolution();
List<Chromosome> chromosomes = (List<Chromosome>) ga.getPopulation();
Collections.sort(chromosomes, new Comparator<Chromosome>() {
@Override
public int compare(Chromosome arg0, Chromosome arg1) {
return Double.compare(arg0.getFitness(f1), arg1.getFitness(f1));
}
});
for (Chromosome chromosome : chromosomes) Assert.assertEquals(chromosome.getFitness(f1), 0.29, 0.01);
for (Chromosome chromosome : chromosomes) {
NSGAChromosome nsga_c = (NSGAChromosome) chromosome;
DoubleVariable x = (DoubleVariable) nsga_c.getVariables().get(0);
DoubleVariable y = (DoubleVariable) nsga_c.getVariables().get(1);
System.out.printf("%f,%f : %f\n", x.getValue(), y.getValue(), chromosome.getFitness(f1));
}
}
use of org.evosuite.ga.variables.DoubleVariable in project evosuite by EvoSuite.
the class TestShere method testSphere.
/**
* Testing NSGA-II with Sphere Problem
*
* @throws IOException
* @throws NumberFormatException
*/
@Test
public void testSphere() throws NumberFormatException, IOException {
Properties.MUTATION_RATE = 1d / 1d;
ChromosomeFactory<?> factory = new RandomFactory(false, 1, Math.pow(-10.0, 3.0), Math.pow(10.0, 3.0));
// GeneticAlgorithm<?> ga = new NSGAII(factory);
GeneticAlgorithm<?> ga = new NSGAII(factory);
BinaryTournamentSelectionCrowdedComparison ts = new BinaryTournamentSelectionCrowdedComparison();
// BinaryTournament ts = new BinaryTournament();
ga.setSelectionFunction(ts);
ga.setCrossOverFunction(new SBXCrossover());
Problem p = new Sphere();
final FitnessFunction f1 = (FitnessFunction) p.getFitnessFunctions().get(0);
ga.addFitnessFunction(f1);
// execute
ga.generateSolution();
List<Chromosome> chromosomes = (List<Chromosome>) ga.getPopulation();
Collections.sort(chromosomes, new Comparator<Chromosome>() {
@Override
public int compare(Chromosome arg0, Chromosome arg1) {
return Double.compare(arg0.getFitness(f1), arg1.getFitness(f1));
}
});
for (Chromosome chromosome : chromosomes) Assert.assertEquals(chromosome.getFitness(f1), 0.00, 0.01);
for (Chromosome chromosome : chromosomes) {
NSGAChromosome nsga_c = (NSGAChromosome) chromosome;
DoubleVariable x = (DoubleVariable) nsga_c.getVariables().get(0);
System.out.printf("%f : %f\n", x.getValue(), chromosome.getFitness(f1));
}
}
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