1 package net.bmahe.genetics4j.neat.spec.combination;
2
3 import org.apache.commons.lang3.Validate;
4 import org.immutables.value.Value;
5
6 import net.bmahe.genetics4j.core.spec.combination.CombinationPolicy;
7 import net.bmahe.genetics4j.neat.spec.combination.parentcompare.ParentComparisonPolicy;
8 import net.bmahe.genetics4j.neat.spec.combination.parentcompare.FitnessComparison;
9
10 /**
11 * Configuration policy for NEAT (NeuroEvolution of Augmenting Topologies) genetic crossover operations.
12 *
13 * <p>NeatCombination defines how neural network chromosomes should be recombined during genetic crossover, including
14 * inheritance biases, gene re-enabling policies, and parent comparison strategies. This policy controls the fundamental
15 * genetic operators that shape network topology evolution in NEAT.
16 *
17 * <p>Key crossover parameters:
18 * <ul>
19 * <li><strong>Inheritance threshold</strong>: Bias toward fitter parent for gene inheritance</li>
20 * <li><strong>Gene re-enabling</strong>: Probability of re-enabling disabled genes during crossover</li>
21 * <li><strong>Parent comparison</strong>: Strategy for determining relative parent fitness</li>
22 * <li><strong>Genetic alignment</strong>: Innovation-number-based gene matching</li>
23 * </ul>
24 *
25 * <p>NEAT genetic crossover process:
26 * <ol>
27 * <li><strong>Gene alignment</strong>: Match genes by innovation number between parents</li>
28 * <li><strong>Matching genes</strong>: Randomly inherit from either parent (biased by inheritance threshold)</li>
29 * <li><strong>Disjoint genes</strong>: Inherit from fitter parent based on parent comparison</li>
30 * <li><strong>Excess genes</strong>: Inherit from fitter parent beyond less fit parent's range</li>
31 * <li><strong>Gene state</strong>: Apply re-enabling policy to disabled genes</li>
32 * </ol>
33 *
34 * <p>Gene inheritance strategies:
35 * <ul>
36 * <li><strong>Matching genes</strong>: Present in both parents with same innovation number</li>
37 * <li><strong>Disjoint genes</strong>: Present in one parent within other parent's innovation range</li>
38 * <li><strong>Excess genes</strong>: Present in one parent beyond other parent's highest innovation</li>
39 * <li><strong>Disabled genes</strong>: May be re-enabled based on re-enabling threshold</li>
40 * </ul>
41 *
42 * <p>Common usage patterns:
43 *
44 * <pre>{@code
45 * // Default NEAT crossover configuration
46 * NeatCombination defaultPolicy = NeatCombination.build();
47 *
48 * // Custom crossover with fitness bias
49 * NeatCombination biasedPolicy = NeatCombination.builder()
50 * .inheritanceThresold(0.7) // 70% bias toward fitter parent
51 * .reenableGeneInheritanceThresold(0.3) // 30% chance to re-enable genes
52 * .parentComparisonPolicy(FitnessComparison.build())
53 * .build();
54 *
55 * // Unbiased crossover for diversity
56 * NeatCombination unbiasedPolicy = NeatCombination.builder()
57 * .inheritanceThresold(0.5) // No bias toward either parent
58 * .reenableGeneInheritanceThresold(0.1) // Low re-enabling rate
59 * .build();
60 *
61 * // Use in EA configuration
62 * var combinationSpec = ChromosomeCombinatorSpec.builder()
63 * .combinationPolicy(biasedPolicy)
64 * .build();
65 * }</pre>
66 *
67 * <p>Inheritance threshold effects:
68 * <ul>
69 * <li><strong>0.5 (default)</strong>: Unbiased inheritance, equal probability from both parents</li>
70 * <li><strong>> 0.5</strong>: Bias toward fitter parent, promotes convergence</li>
71 * <li><strong>< 0.5</strong>: Bias toward less fit parent, increases diversity</li>
72 * <li><strong>1.0</strong>: Always inherit from fitter parent (if determinable)</li>
73 * </ul>
74 *
75 * <p>Gene re-enabling mechanism:
76 * <ul>
77 * <li><strong>Historical information</strong>: Disabled genes preserve connection topology</li>
78 * <li><strong>Re-activation chance</strong>: Allows previously disabled connections to contribute again</li>
79 * <li><strong>Topology exploration</strong>: Enables rediscovery of useful connection patterns</li>
80 * <li><strong>Genetic diversity</strong>: Prevents permanent loss of structural information</li>
81 * </ul>
82 *
83 * <p>Parent comparison integration:
84 * <ul>
85 * <li><strong>Fitness comparison</strong>: Standard fitness-based parent ranking</li>
86 * <li><strong>Custom strategies</strong>: Pluggable comparison policies for different problem domains</li>
87 * <li><strong>Multi-objective support</strong>: Compatible with complex fitness landscapes</li>
88 * <li><strong>Equal fitness handling</strong>: Special rules when parents have identical fitness</li>
89 * </ul>
90 *
91 * <p>Performance considerations:
92 * <ul>
93 * <li><strong>Innovation sorting</strong>: Leverages pre-sorted connection lists for O(n) crossover</li>
94 * <li><strong>Memory efficiency</strong>: Minimal allocation during gene inheritance</li>
95 * <li><strong>Cache-friendly</strong>: Sequential access patterns for better cache performance</li>
96 * <li><strong>Parallelizable</strong>: Crossover operations can be executed concurrently</li>
97 * </ul>
98 *
99 * @see ParentComparisonPolicy
100 * @see FitnessComparison
101 * @see net.bmahe.genetics4j.neat.combination.NeatChromosomeCombinator
102 * @see CombinationPolicy
103 */
104 @Value.Immutable
105 public interface NeatCombination extends CombinationPolicy {
106
107 public static final double DEFAULT_INHERITANCE_THRESHOLD = 0.5d;
108
109 public static final double DEFAULT_REENABLE_GENE_INHERITANCE_THRESHOLD = 0.25d;
110
111 /**
112 * Returns the inheritance threshold for biasing gene selection toward fitter parents.
113 *
114 * <p>This threshold controls the probability of inheriting genes from the fitter parent during crossover. Higher
115 * values bias inheritance toward the better performing parent, while lower values provide more equal inheritance or
116 * even bias toward the less fit parent.
117 *
118 * <p>Inheritance behavior:
119 * <ul>
120 * <li><strong>0.5 (default)</strong>: Unbiased inheritance, equal probability from both parents</li>
121 * <li><strong>> 0.5</strong>: Bias toward fitter parent, promotes convergence to good solutions</li>
122 * <li><strong>< 0.5</strong>: Bias toward less fit parent, increases population diversity</li>
123 * <li><strong>1.0</strong>: Always inherit from fitter parent when fitness differs</li>
124 * <li><strong>0.0</strong>: Always inherit from less fit parent when fitness differs</li>
125 * </ul>
126 *
127 * @return inheritance threshold value between 0.0 and 1.0 (inclusive)
128 */
129 @Value.Default
130 default public double inheritanceThresold() {
131 return DEFAULT_INHERITANCE_THRESHOLD;
132 }
133
134 /**
135 * Returns the threshold for re-enabling disabled genes during crossover.
136 *
137 * <p>When a gene (connection) is disabled in one parent but enabled in the other, this threshold determines the
138 * probability that the gene will be enabled in the offspring. This mechanism prevents permanent loss of potentially
139 * useful connections and allows rediscovery of structural innovations.
140 *
141 * <p>Re-enabling behavior:
142 * <ul>
143 * <li><strong>0.25 (default)</strong>: 25% chance to re-enable disabled connections</li>
144 * <li><strong>0.0</strong>: Never re-enable disabled connections</li>
145 * <li><strong>1.0</strong>: Always re-enable connections that are enabled in either parent</li>
146 * <li><strong>Higher values</strong>: More aggressive topology exploration</li>
147 * <li><strong>Lower values</strong>: More conservative structural preservation</li>
148 * </ul>
149 *
150 * @return re-enabling threshold value between 0.0 and 1.0 (inclusive)
151 */
152 @Value.Default
153 default public double reenableGeneInheritanceThresold() {
154 return DEFAULT_REENABLE_GENE_INHERITANCE_THRESHOLD;
155 }
156
157 /**
158 * Returns the policy used to compare parent fitness for inheritance decisions.
159 *
160 * <p>The parent comparison policy determines which parent is considered "fitter" for the purposes of biased gene
161 * inheritance. This affects how disjoint and excess genes are inherited and how the inheritance threshold is
162 * applied.
163 *
164 * <p>Available comparison strategies:
165 * <ul>
166 * <li><strong>FitnessComparison (default)</strong>: Compare parents based on their fitness values</li>
167 * <li><strong>Custom policies</strong>: Pluggable strategies for domain-specific comparisons</li>
168 * <li><strong>Multi-objective</strong>: Specialized comparisons for multi-objective optimization</li>
169 * <li><strong>Equal fitness handling</strong>: Specific behavior when parents have identical fitness</li>
170 * </ul>
171 *
172 * @return the parent comparison policy (defaults to fitness-based comparison)
173 */
174 @Value.Default
175 default public ParentComparisonPolicy parentComparisonPolicy() {
176 return FitnessComparison.build();
177 }
178
179 @Value.Check
180 default void check() {
181 Validate.inclusiveBetween(0, 1, inheritanceThresold());
182 Validate.inclusiveBetween(0, 1, reenableGeneInheritanceThresold());
183 }
184
185 class Builder extends ImmutableNeatCombination.Builder {
186 }
187
188 static Builder builder() {
189 return new Builder();
190 }
191
192 /**
193 * Creates a NEAT combination policy with default settings.
194 *
195 * <p>Default configuration:
196 * <ul>
197 * <li>Inheritance threshold: 0.5 (unbiased)</li>
198 * <li>Gene re-enabling threshold: 0.25 (25% chance)</li>
199 * <li>Parent comparison: Fitness-based comparison</li>
200 * </ul>
201 *
202 * @return a new NEAT combination policy with default settings
203 */
204 static NeatCombination build() {
205 return builder().build();
206 }
207
208 }