NeatConnectionWeightPolicyHandler.java

package net.bmahe.genetics4j.neat.mutation;

import java.util.Objects;

import java.util.random.RandomGenerator;

import net.bmahe.genetics4j.core.chromosomes.Chromosome;

import net.bmahe.genetics4j.core.mutation.GenericMutatorImpl;

import net.bmahe.genetics4j.core.mutation.MutationPolicyHandler;

import net.bmahe.genetics4j.core.mutation.MutationPolicyHandlerResolver;

import net.bmahe.genetics4j.core.mutation.Mutator;

import net.bmahe.genetics4j.core.mutation.chromosome.ChromosomeMutationHandler;

import net.bmahe.genetics4j.core.spec.AbstractEAConfiguration;

import net.bmahe.genetics4j.core.spec.AbstractEAExecutionContext;

import net.bmahe.genetics4j.core.spec.mutation.MutationPolicy;

import net.bmahe.genetics4j.core.util.ChromosomeResolverUtils;

import net.bmahe.genetics4j.neat.spec.mutation.NeatConnectionWeight;

/**

 * Mutation policy handler for NEAT (NeuroEvolution of Augmenting Topologies) connection weight mutations.

 * 

 * <p>NeatConnectionWeightPolicyHandler manages weight mutations for NEAT neural network connections, which are

 * essential for fine-tuning network behavior and optimizing performance. Weight mutations are typically the most

 * frequent type of mutation in NEAT evolution, applied to existing connections to improve network functionality.

 * 

 * <p>Weight mutation strategies:

 * <ul>

 * <li><strong>Gaussian perturbation</strong>: Add small random values to existing weights</li>

 * <li><strong>Random replacement</strong>: Replace weights with new random values</li>

 * <li><strong>Creep mutation</strong>: Small incremental changes to weights</li>

 * <li><strong>Uniform perturbation</strong>: Add uniform random noise to weights</li>

 * </ul>

 * 

 * <p>Key characteristics:

 * <ul>

 * <li><strong>Behavioral optimization</strong>: Fine-tunes network behavior without changing topology</li>

 * <li><strong>High frequency</strong>: Applied more often than structural mutations</li>

 * <li><strong>Bounded values</strong>: Respects chromosome weight bounds during mutation</li>

 * <li><strong>Continuous evolution</strong>: Enables continuous improvement of network performance</li>

 * </ul>

 * 

 * <p>Integration with NEAT algorithm:

 * <ul>

 * <li><strong>Chromosome mutation</strong>: Delegates to NEAT chromosome weight mutation handlers</li>

 * <li><strong>Population evolution</strong>: Applied based on configured mutation probability</li>

 * <li><strong>Performance optimization</strong>: Primary mechanism for network performance tuning</li>

 * <li><strong>Genetic diversity</strong>: Maintains behavioral diversity in the population</li>

 * </ul>

 * 

 * <p>Common usage patterns:

 * 

 * <pre>{@code

 * // Create weight mutation policy

 * NeatConnectionWeight weightPolicy = NeatConnectionWeight.builder()

 * 		.populationMutationProbability(0.8) // 80% of population

 * 		.chromosomeMutationProbability(0.9) // 90% of connections per chromosome

 * 		.mutationStandardDeviation(0.1) // Standard deviation for Gaussian perturbation

 * 		.build();

 * 

 * // Create policy handler

 * RandomGenerator randomGen = RandomGenerator.getDefault();

 * NeatConnectionWeightPolicyHandler<Double> handler = new NeatConnectionWeightPolicyHandler<>(randomGen);

 * 

 * // Check if handler can process the policy

 * boolean canHandle = handler.canHandle(resolver, weightPolicy);

 * 

 * // Create mutator for the policy

 * Mutator mutator = handler.createMutator(executionContext, configuration, resolver, weightPolicy);

 * }</pre>

 * 

 * <p>Weight mutation effects:

 * <ul>

 * <li><strong>Performance tuning</strong>: Optimizes network output for better fitness</li>

 * <li><strong>Exploration vs exploitation</strong>: Balances exploration of weight space with convergence</li>

 * <li><strong>Gradient-like behavior</strong>: Can simulate gradient-based optimization</li>

 * <li><strong>Population diversity</strong>: Maintains diverse behavioral patterns</li>

 * </ul>

 * 

 * <p>Mutation policy configuration:

 * <ul>

 * <li><strong>Population probability</strong>: Fraction of population to apply mutations to</li>

 * <li><strong>Chromosome probability</strong>: Fraction of connections to mutate per chromosome</li>

 * <li><strong>Mutation magnitude</strong>: Size of weight perturbations</li>

 * <li><strong>Mutation type</strong>: Strategy for weight modification (Gaussian, uniform, etc.)</li>

 * </ul>

 * 

 * <p>Performance considerations:

 * <ul>

 * <li><strong>High frequency application</strong>: Efficient implementation for frequent use</li>

 * <li><strong>Memory efficiency</strong>: Minimal allocation during weight modification</li>

 * <li><strong>Numerical stability</strong>: Maintains weight values within valid ranges</li>

 * <li><strong>Concurrent safety</strong>: Thread-safe for parallel mutation application</li>

 * </ul>

 * 

 * @param <T> the fitness value type (typically Double)

 * @see NeatConnectionWeight

 * @see net.bmahe.genetics4j.neat.mutation.chromosome.NeatChromosomeConnectionWeightMutationHandler

 * @see MutationPolicyHandler

 */

public class NeatConnectionWeightPolicyHandler<T extends Comparable<T>> implements MutationPolicyHandler<T> {

	private final RandomGenerator randomGenerator;

	/**

	 * Constructs a new connection weight policy handler with the specified random generator.

	 * 

	 * <p>The random generator is used for stochastic decisions during mutation application, including selection of

	 * individuals to mutate and generation of weight perturbations.

	 * 

	 * @param _randomGenerator random number generator for stochastic mutation operations

	 * @throws IllegalArgumentException if randomGenerator is null

	 */

	/**

	 * Determines whether this handler can process the given mutation policy.

	 * 

	 * <p>This handler specifically processes NeatConnectionWeight mutation policies, which configure the parameters for

	 * mutating connection weights in NEAT neural networks.

	 * 

	 * @param mutationPolicyHandlerResolver resolver for nested mutation policies

	 * @param mutationPolicy                the mutation policy to check

	 * @return true if the policy is a NeatConnectionWeight instance, false otherwise

	 * @throws IllegalArgumentException if any parameter is null

	 */

	@Override

	public boolean canHandle(final MutationPolicyHandlerResolver<T> mutationPolicyHandlerResolver,

			final MutationPolicy mutationPolicy) {

	}

	/**

	 * Creates a concrete mutator for connection weight mutations.

	 * 

	 * <p>This method resolves the appropriate chromosome mutation handlers for NEAT chromosomes and creates a generic

	 * mutator that applies weight mutations according to the specified policy parameters.

	 * 

	 * <p>Mutator creation process:

	 * <ol>

	 * <li>Extract population mutation probability from the policy</li>

	 * <li>Resolve chromosome-specific mutation handlers</li>

	 * <li>Create generic mutator with resolved components</li>

	 * <li>Return configured mutator ready for population application</li>

	 * </ol>

	 * 

	 * @param eaExecutionContext            execution context containing NEAT-specific components

	 * @param eaConfiguration               evolutionary algorithm configuration

	 * @param mutationPolicyHandlerResolver resolver for chromosome mutation handlers

	 * @param mutationPolicy                the connection weight mutation policy

	 * @return a configured mutator for applying weight mutations

	 * @throws IllegalArgumentException if any parameter is null

	 */

	@Override

	public Mutator createMutator(final AbstractEAExecutionContext<T> eaExecutionContext,

			final AbstractEAConfiguration<T> eaConfiguration,

			final MutationPolicyHandlerResolver<T> mutationPolicyHandlerResolver, MutationPolicy mutationPolicy) {

				chromosomeMutationHandlers,

				mutationPolicy,

				populationMutationProbability);

	}

}

Mutations

1.1
Location : createMutator
Killed by : none removed call to net/bmahe/genetics4j/neat/spec/mutation/NeatConnectionWeight::populationMutationProbability → SURVIVED Covering tests