AbstractEAConfiguration.java

package net.bmahe.genetics4j.core.spec;

import java.util.Collection;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;
import java.util.Optional;
import java.util.function.Supplier;

import org.apache.commons.lang3.Validate;
import org.immutables.value.Value;

import net.bmahe.genetics4j.core.Genotype;
import net.bmahe.genetics4j.core.combination.AllCasesGenotypeCombinator;
import net.bmahe.genetics4j.core.combination.GenotypeCombinator;
import net.bmahe.genetics4j.core.spec.chromosome.ChromosomeSpec;
import net.bmahe.genetics4j.core.spec.combination.CombinationPolicy;
import net.bmahe.genetics4j.core.spec.mutation.MutationPolicy;
import net.bmahe.genetics4j.core.spec.replacement.Elitism;
import net.bmahe.genetics4j.core.spec.replacement.ReplacementStrategy;
import net.bmahe.genetics4j.core.spec.selection.SelectionPolicy;
import net.bmahe.genetics4j.core.spec.selection.Tournament;
import net.bmahe.genetics4j.core.termination.Termination;

/**
 * Evolutionary Algorithm Configuration.
 * <p>This describe the set of strategies to use. They describe the genotype, the different policies for selection,
 * combination as well as mutation, and other relevant parameters
 * <p>Fitness computation is delegated to subclasses to better match the various ways in which they can be computed
 * 
 * @param <T> Type of the fitness measurement
 */
public abstract class AbstractEAConfiguration<T extends Comparable<T>> {
	/**
	 * Default offspring ratio
	 */
	public static final double DEFAULT_OFFSPRING_RATIO = 1.0;

	/**
	 * Default optimization strategy
	 */
	public static final Optimization DEFAULT_OPTIMIZATION = Optimization.MAXIMIZE;

	/**
	 * Genotype of the population
	 * 
	 * @return
	 */
	public abstract List<ChromosomeSpec> chromosomeSpecs();

	/**
	 * Defines the policy to select the parents. The selected parents will be used for generating the new offsprings
	 * 
	 * @return
	 */
	public abstract SelectionPolicy parentSelectionPolicy();

	/**
	 * Defines the policy to generate new offsprings from two parents
	 * 
	 * @return
	 */
	public abstract CombinationPolicy combinationPolicy();

	/**
	 * Defines what mutations to be performed on the offsprings
	 * 
	 * @return
	 */
	public abstract List<MutationPolicy> mutationPolicies();

	/**
	 * Defines the replacement strategy
	 * <p>The replacement strategy is what will determine the next population based on the generated and mutated
	 * offsprings along with the current population
	 * <p>If not specified, the default replacement strategy will be to use Elitism with tournament selection of 3
	 * individuals for both offsprings and survivors. The default offspring ratio is
	 * {@link Elitism#DEFAULT_OFFSPRING_RATIO}
	 * 
	 * @return
	 */
	@Value.Default
	public ReplacementStrategy replacementStrategy() {
		final var replacementStrategyBuilder = Elitism.builder();

		replacementStrategyBuilder.offspringRatio(Elitism.DEFAULT_OFFSPRING_RATIO)
				.offspringSelectionPolicy(Tournament.of(3))
				.survivorSelectionPolicy(Tournament.of(3));

		return replacementStrategyBuilder.build();
	}

	/**
	 * Post-processing of a population after it got evaluated
	 * <p>This gives the opportunity to filter out, repair or rescore individuals
	 * 
	 * @return Population to be used by the remaining evolution process
	 */
	public abstract Optional<PostEvaluationProcessor<T>> postEvaluationProcessor();

	/**
	 * Defines termination condition
	 * 
	 * @return
	 */
	public abstract Termination<T> termination();

	/**
	 * Defines how to generate individuals
	 * <p>If not specified, the system will rely on the chromosome factories
	 * 
	 * @return
	 */
	public abstract Optional<Supplier<Genotype>> genotypeGenerator();

	/**
	 * Seed the initial population with specific individuals
	 * 
	 * @return
	 */
	@Value.Default
	public Collection<Genotype> seedPopulation() {
		return Collections.emptyList();
	}

	/**
	 * Defines how to combine the offspring chromosomes generated
	 * <p>Combination of individuals is done on a per chromosome basis. This means some parents may generate a different
	 * number of children for each chromosome. This method will therefore define how to take all these generated
	 * chromosomes and combine them into offspring individuals
	 * <p>The current default implementation is to generate as many individual as there are combinations of generated
	 * chromosomes
	 * 
	 * @return
	 */
	@Value.Default
	public GenotypeCombinator genotypeCombinator() {
		return new AllCasesGenotypeCombinator();
	}

	/**
	 * Defines how many children will be generated at each iteration. Value must be between 0 and 1 (inclusive) and
	 * represents a fraction of the population size
	 * 
	 * @return
	 */
	@Value.Default
	public double offspringGeneratedRatio() {
		return DEFAULT_OFFSPRING_RATIO;
	}

	/**
	 * Defines the optimization goal, whether we want to maximize the fitness or minimize it
	 * 
	 * @return
	 */
	@Value.Default
	public Optimization optimization() {
		return DEFAULT_OPTIMIZATION;
	}

	/**
	 * Validates the configuration
	 */
	@Value.Check
	protected void check() {
		Validate.isTrue(chromosomeSpecs().size() > 0, "No chromosomes were specified");
		Validate.inclusiveBetween(0.0d, 1.0d, offspringGeneratedRatio());
	}

	/**
	 * Returns a specific chromosome spec from the genotype definition
	 * 
	 * @param index
	 * @return
	 */
	public ChromosomeSpec getChromosomeSpec(final int index) {
		Validate.isTrue(index >= 0);
		Validate.isTrue(index < chromosomeSpecs().size());

		return chromosomeSpecs().get(index);
	}

	/**
	 * Returns the currently number of chromosomes defined in the genotype
	 * 
	 * @return
	 */
	public int numChromosomes() {
		return chromosomeSpecs().size();
	}

	/**
	 * Return a comparator based on the optimization method and natural order
	 * 
	 * @return
	 */
	public Comparator<T> fitnessComparator() {

		return switch (optimization()) {
			case MAXIMIZE -> Comparator.naturalOrder();
			case MINIMIZE -> Comparator.reverseOrder();
		};
	}
}