1 package net.bmahe.genetics4j.core.spec.mutation;
2
3 /**
4 * Marker interface for mutation policy specifications in evolutionary algorithms.
5 *
6 * <p>MutationPolicy defines the contract for specifying mutation strategies and their parameters. Mutation policies
7 * determine how genetic variation is introduced into the population by making random changes to individual genotypes.
8 * Different mutation policies implement various strategies for balancing exploration and exploitation during the
9 * evolutionary process.
10 *
11 * <p>Mutation policies are used by:
12 * <ul>
13 * <li><strong>EA Configuration</strong>: To specify the mutation strategy for the algorithm</li>
14 * <li><strong>Mutation handlers</strong>: To create appropriate mutator implementations</li>
15 * <li><strong>Strategy resolution</strong>: To select chromosome-specific mutation operators</li>
16 * <li><strong>Parameter configuration</strong>: To define mutation rates and other parameters</li>
17 * </ul>
18 *
19 * <p>The framework provides several concrete mutation policy implementations:
20 * <ul>
21 * <li>{@link RandomMutation}: Randomly changes allele values with specified probability</li>
22 * <li>{@link CreepMutation}: Makes small incremental changes to numeric values</li>
23 * <li>{@link SwapMutation}: Exchanges positions of alleles (useful for permutations)</li>
24 * <li>{@link PartialMutation}: Applies mutation to only a subset of chromosomes</li>
25 * <li>{@link MultiMutations}: Combines multiple mutation strategies</li>
26 * <li>{@link RepeatedMutation}: Applies another mutation strategy repeatedly</li>
27 * </ul>
28 *
29 * <p>Mutation strategies vary by chromosome type and problem domain:
30 * <ul>
31 * <li><strong>Binary chromosomes</strong>: Bit flip mutations</li>
32 * <li><strong>Numeric chromosomes</strong>: Gaussian noise, uniform perturbation, creep mutation</li>
33 * <li><strong>Permutation chromosomes</strong>: Swap, insert, inversion mutations</li>
34 * <li><strong>Tree chromosomes</strong>: Node replacement, subtree mutation, growth/pruning</li>
35 * </ul>
36 *
37 * <p>Key considerations for mutation policy design:
38 * <ul>
39 * <li><strong>Mutation rate</strong>: Balance between exploration and exploitation</li>
40 * <li><strong>Chromosome compatibility</strong>: Ensure mutation respects chromosome constraints</li>
41 * <li><strong>Problem-specific operations</strong>: Adapt mutation to problem characteristics</li>
42 * <li><strong>Adaptive strategies</strong>: Allow mutation parameters to evolve during the run</li>
43 * </ul>
44 *
45 * <p>Example usage in genetic algorithm configuration:
46 *
47 * <pre>{@code
48 * // Random mutation with 5% probability per allele
49 * MutationPolicy randomMutation = RandomMutation.of(0.05);
50 *
51 * // Creep mutation for numeric optimization
52 * MutationPolicy creepMutation = CreepMutation.of(0.1, 1.0);
53 *
54 * // Combination of multiple mutation strategies
55 * MutationPolicy multiMutation = MultiMutations.of(Tuple.of(0.7, randomMutation), Tuple.of(0.3, creepMutation));
56 *
57 * // Use in EA configuration
58 * EAConfiguration<Double> config = EAConfigurationBuilder.<Double>builder()
59 * .chromosomeSpecs(chromosomeSpec)
60 * .mutationPolicy(randomMutation)
61 * .build();
62 * }</pre>
63 *
64 * @see net.bmahe.genetics4j.core.mutation.Mutator
65 * @see net.bmahe.genetics4j.core.mutation.MutationPolicyHandler
66 * @see RandomMutation
67 * @see CreepMutation
68 * @see SwapMutation
69 * @see PartialMutation
70 * @see MultiMutations
71 * @see RepeatedMutation
72 */
73 public interface MutationPolicy {
74
75 }