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package jcgp.backend.modules.es;
import jcgp.backend.modules.mutator.Mutator;
import jcgp.backend.parameters.BooleanParameter;
import jcgp.backend.parameters.IntegerParameter;
import jcgp.backend.parameters.ParameterStatus;
import jcgp.backend.population.Population;
import jcgp.backend.resources.Resources;
/**
* (μ + λ)-ES
* <br><br>
* This strategy selects the μ fittest chromosomes from the population.
* The promoted individuals are copied into the new population and mutated
* λ times, but also carried forward unchanged. The total population size
* is μ + λ.
* <br><br>
* Two integer parameters are used to control this strategy: parents
* and offspring. They are constrained in that they must always add up to
* the population size, and must never be smaller than 1.
* <br>
* One additional parameter, report, controls whether a detailed log of the
* algorithm's operation is to be printed or not. Reports respect the report
* interval base parameter.
*
* @see EvolutionaryStrategy
* @author Eduardo Pedroni
*
*/
public class MuPlusLambda extends EvolutionaryStrategy {
private IntegerParameter mu, lambda;
private BooleanParameter report;
/**
* Creates a new instance of MuPlusLambda.
*
* @param resources a reference to the experiment's resources.
*/
public MuPlusLambda(final Resources resources) {
super(resources);
mu = new IntegerParameter(1, "Parents (μ)") {
@Override
public void validate(Number newValue) {
if (newValue.intValue() + lambda.get() != getResources().populationSize()) {
status = ParameterStatus.INVALID;
status.setDetails("Parents + offspring must equal population size.");
} else if (newValue.intValue() <= 0) {
status = ParameterStatus.INVALID;
status.setDetails("ES needs at least 1 parent.");
} else {
status = ParameterStatus.VALID;
}
}
};
lambda = new IntegerParameter(4, "Offspring (λ)") {
@Override
public void validate(Number newValue) {
if (newValue.intValue() + mu.get() != getResources().populationSize()) {
status = ParameterStatus.INVALID;
status.setDetails("Parents + offspring must equal population size.");
} else if (newValue.intValue() <= 0) {
status = ParameterStatus.INVALID;
status.setDetails("ES needs at least 1 offspring.");
} else {
status = ParameterStatus.VALID;
}
}
};
report = new BooleanParameter(false, "Report");
setName("(μ + λ)");
registerParameters(mu, lambda, report);
}
@Override
public void evolve(Population population, Mutator mutator) {
// sort the population in order of best fitness
population.sort();
// population is now sorted in ascending order of fitness, the last chromosomes are the fittest
for (int i = 0; i < getResources().populationSize() - mu.get(); i++) {
// select a random parent out of the lambda offspring individuals
int randomParent = getResources().populationSize() - 1 - getResources().getRandomInt(mu.get());
if (report.get()) getResources().reportln("[ES] Copying Chr " + randomParent + " to population position " + i);
population.copyChromosome(randomParent, i);
// mutate selected chromosome
if (report.get()) getResources().reportln("[ES] Mutating copied chromosome");
mutator.mutate(population.get(i));
}
if (report.get()) getResources().reportln("[ES] Generation is complete");
}
}
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