NeatCombination.java

package net.bmahe.genetics4j.neat.spec.combination;

import org.apache.commons.lang3.Validate;

import org.immutables.value.Value;

import net.bmahe.genetics4j.core.spec.combination.CombinationPolicy;

import net.bmahe.genetics4j.neat.spec.combination.parentcompare.ParentComparisonPolicy;

import net.bmahe.genetics4j.neat.spec.combination.parentcompare.FitnessComparison;

/**

 * Configuration policy for NEAT (NeuroEvolution of Augmenting Topologies) genetic crossover operations.

 * 

 * <p>NeatCombination defines how neural network chromosomes should be recombined during genetic crossover, including

 * inheritance biases, gene re-enabling policies, and parent comparison strategies. This policy controls the fundamental

 * genetic operators that shape network topology evolution in NEAT.

 * 

 * <p>Key crossover parameters:

 * <ul>

 * <li><strong>Inheritance threshold</strong>: Bias toward fitter parent for gene inheritance</li>

 * <li><strong>Gene re-enabling</strong>: Probability of re-enabling disabled genes during crossover</li>

 * <li><strong>Parent comparison</strong>: Strategy for determining relative parent fitness</li>

 * <li><strong>Genetic alignment</strong>: Innovation-number-based gene matching</li>

 * </ul>

 * 

 * <p>NEAT genetic crossover process:

 * <ol>

 * <li><strong>Gene alignment</strong>: Match genes by innovation number between parents</li>

 * <li><strong>Matching genes</strong>: Randomly inherit from either parent (biased by inheritance threshold)</li>

 * <li><strong>Disjoint genes</strong>: Inherit from fitter parent based on parent comparison</li>

 * <li><strong>Excess genes</strong>: Inherit from fitter parent beyond less fit parent's range</li>

 * <li><strong>Gene state</strong>: Apply re-enabling policy to disabled genes</li>

 * </ol>

 * 

 * <p>Gene inheritance strategies:

 * <ul>

 * <li><strong>Matching genes</strong>: Present in both parents with same innovation number</li>

 * <li><strong>Disjoint genes</strong>: Present in one parent within other parent's innovation range</li>

 * <li><strong>Excess genes</strong>: Present in one parent beyond other parent's highest innovation</li>

 * <li><strong>Disabled genes</strong>: May be re-enabled based on re-enabling threshold</li>

 * </ul>

 * 

 * <p>Common usage patterns:

 * 

 * <pre>{@code

 * // Default NEAT crossover configuration

 * NeatCombination defaultPolicy = NeatCombination.build();

 * 

 * // Custom crossover with fitness bias

 * NeatCombination biasedPolicy = NeatCombination.builder()

 * 		.inheritanceThresold(0.7) // 70% bias toward fitter parent

 * 		.reenableGeneInheritanceThresold(0.3) // 30% chance to re-enable genes

 * 		.parentComparisonPolicy(FitnessComparison.build())

 * 		.build();

 * 

 * // Unbiased crossover for diversity

 * NeatCombination unbiasedPolicy = NeatCombination.builder()

 * 		.inheritanceThresold(0.5) // No bias toward either parent

 * 		.reenableGeneInheritanceThresold(0.1) // Low re-enabling rate

 * 		.build();

 * 

 * // Use in EA configuration

 * var combinationSpec = ChromosomeCombinatorSpec.builder().combinationPolicy(biasedPolicy).build();

 * }</pre>

 * 

 * <p>Inheritance threshold effects:

 * <ul>

 * <li><strong>0.5 (default)</strong>: Unbiased inheritance, equal probability from both parents</li>

 * <li><strong>&gt; 0.5</strong>: Bias toward fitter parent, promotes convergence</li>

 * <li><strong>&lt; 0.5</strong>: Bias toward less fit parent, increases diversity</li>

 * <li><strong>1.0</strong>: Always inherit from fitter parent (if determinable)</li>

 * </ul>

 * 

 * <p>Gene re-enabling mechanism:

 * <ul>

 * <li><strong>Historical information</strong>: Disabled genes preserve connection topology</li>

 * <li><strong>Re-activation chance</strong>: Allows previously disabled connections to contribute again</li>

 * <li><strong>Topology exploration</strong>: Enables rediscovery of useful connection patterns</li>

 * <li><strong>Genetic diversity</strong>: Prevents permanent loss of structural information</li>

 * </ul>

 * 

 * <p>Parent comparison integration:

 * <ul>

 * <li><strong>Fitness comparison</strong>: Standard fitness-based parent ranking</li>

 * <li><strong>Custom strategies</strong>: Pluggable comparison policies for different problem domains</li>

 * <li><strong>Multi-objective support</strong>: Compatible with complex fitness landscapes</li>

 * <li><strong>Equal fitness handling</strong>: Special rules when parents have identical fitness</li>

 * </ul>

 * 

 * <p>Performance considerations:

 * <ul>

 * <li><strong>Innovation sorting</strong>: Leverages pre-sorted connection lists for O(n) crossover</li>

 * <li><strong>Memory efficiency</strong>: Minimal allocation during gene inheritance</li>

 * <li><strong>Cache-friendly</strong>: Sequential access patterns for better cache performance</li>

 * <li><strong>Parallelizable</strong>: Crossover operations can be executed concurrently</li>

 * </ul>

 * 

 * @see ParentComparisonPolicy

 * @see FitnessComparison

 * @see net.bmahe.genetics4j.neat.combination.NeatChromosomeCombinator

 * @see CombinationPolicy

 */

@Value.Immutable

public interface NeatCombination extends CombinationPolicy {

	public static final double DEFAULT_INHERITANCE_THRESHOLD = 0.5d;

	public static final double DEFAULT_REENABLE_GENE_INHERITANCE_THRESHOLD = 0.25d;

	/**

	 * Returns the inheritance threshold for biasing gene selection toward fitter parents.

	 * 

	 * <p>This threshold controls the probability of inheriting genes from the fitter parent during crossover. Higher

	 * values bias inheritance toward the better performing parent, while lower values provide more equal inheritance or

	 * even bias toward the less fit parent.

	 * 

	 * <p>Inheritance behavior:

	 * <ul>

	 * <li><strong>0.5 (default)</strong>: Unbiased inheritance, equal probability from both parents</li>

	 * <li><strong>&gt; 0.5</strong>: Bias toward fitter parent, promotes convergence to good solutions</li>

	 * <li><strong>&lt; 0.5</strong>: Bias toward less fit parent, increases population diversity</li>

	 * <li><strong>1.0</strong>: Always inherit from fitter parent when fitness differs</li>

	 * <li><strong>0.0</strong>: Always inherit from less fit parent when fitness differs</li>

	 * </ul>

	 * 

	 * @return inheritance threshold value between 0.0 and 1.0 (inclusive)

	 */

	@Value.Default

	default public double inheritanceThresold() {

	}

	/**

	 * Returns the threshold for re-enabling disabled genes during crossover.

	 * 

	 * <p>When a gene (connection) is disabled in one parent but enabled in the other, this threshold determines the

	 * probability that the gene will be enabled in the offspring. This mechanism prevents permanent loss of potentially

	 * useful connections and allows rediscovery of structural innovations.

	 * 

	 * <p>Re-enabling behavior:

	 * <ul>

	 * <li><strong>0.25 (default)</strong>: 25% chance to re-enable disabled connections</li>

	 * <li><strong>0.0</strong>: Never re-enable disabled connections</li>

	 * <li><strong>1.0</strong>: Always re-enable connections that are enabled in either parent</li>

	 * <li><strong>Higher values</strong>: More aggressive topology exploration</li>

	 * <li><strong>Lower values</strong>: More conservative structural preservation</li>

	 * </ul>

	 * 

	 * @return re-enabling threshold value between 0.0 and 1.0 (inclusive)

	 */

	@Value.Default

	default public double reenableGeneInheritanceThresold() {

	}

	/**

	 * Returns the policy used to compare parent fitness for inheritance decisions.

	 * 

	 * <p>The parent comparison policy determines which parent is considered "fitter" for the purposes of biased gene

	 * inheritance. This affects how disjoint and excess genes are inherited and how the inheritance threshold is

	 * applied.

	 * 

	 * <p>Available comparison strategies:

	 * <ul>

	 * <li><strong>FitnessComparison (default)</strong>: Compare parents based on their fitness values</li>

	 * <li><strong>Custom policies</strong>: Pluggable strategies for domain-specific comparisons</li>

	 * <li><strong>Multi-objective</strong>: Specialized comparisons for multi-objective optimization</li>

	 * <li><strong>Equal fitness handling</strong>: Specific behavior when parents have identical fitness</li>

	 * </ul>

	 * 

	 * @return the parent comparison policy (defaults to fitness-based comparison)

	 */

	@Value.Default

	default public ParentComparisonPolicy parentComparisonPolicy() {

	}

	@Value.Check

	default void check() {

	}

	static Builder builder() {

	}

	/**

	 * Creates a NEAT combination policy with default settings.

	 * 

	 * <p>Default configuration:

	 * <ul>

	 * <li>Inheritance threshold: 0.5 (unbiased)</li>

	 * <li>Gene re-enabling threshold: 0.25 (25% chance)</li>

	 * <li>Parent comparison: Fitness-based comparison</li>

	 * </ul>

	 * 

	 * @return a new NEAT combination policy with default settings

	 */

	static NeatCombination build() {

	}

}

Mutations

1.1
Location : inheritanceThresold
Killed by : none replaced double return with 0.0d for net/bmahe/genetics4j/neat/spec/combination/NeatCombination::inheritanceThresold → SURVIVED Covering tests

2.2
Location : inheritanceThresold
Killed by : none Substituted 0.5 with 1.0 → SURVIVED Covering tests

1.1
Location : reenableGeneInheritanceThresold
Killed by : none replaced double return with 0.0d for net/bmahe/genetics4j/neat/spec/combination/NeatCombination::reenableGeneInheritanceThresold → SURVIVED Covering tests

2.2
Location : reenableGeneInheritanceThresold
Killed by : none Substituted 0.25 with 1.0 → SURVIVED Covering tests