All Known Subinterfaces:: MultiTournaments<T>, ProportionalTournament<T>

All Known Implementing Classes:: DoubleTournament, ImmutableDoubleTournament, ImmutableMultiSelections, ImmutableMultiTournaments, ImmutableNeatSelection, ImmutableNSGA2Selection, ImmutableProportionalTournament, ImmutableRandomSelection, ImmutableRouletteWheel, ImmutableSelectAll, ImmutableTournament, ImmutableTournamentNSGA2Selection, MultiSelections, NeatSelection, NSGA2Selection, RandomSelection, RouletteWheel, SelectAll, Tournament, TournamentNSGA2Selection

public interface SelectionPolicy

Marker interface for selection policy specifications in evolutionary algorithms.

SelectionPolicy defines the contract for specifying selection strategies and their parameters. Selection policies determine how individuals are chosen from the population for reproduction, directly influencing the evolutionary pressure and convergence characteristics of the algorithm.

Selection policies are used by:

EA Configuration: To specify the parent selection strategy
Selection handlers: To create appropriate selector implementations
Strategy resolution: To match policies with their corresponding handlers
Parameter configuration: To define selection pressure and tournament sizes

The framework provides several concrete selection policy implementations:

Tournament: Tournament selection with configurable tournament size
RouletteWheel: Fitness-proportionate selection (roulette wheel)
RandomSelection: Uniform random selection without fitness bias
DoubleTournament: Two-stage tournament for multi-objective optimization
ProportionalTournament: Tournament with proportional selection pressure
MultiSelections: Combination of multiple selection strategies
SelectAll: Selects all individuals (useful for specific algorithms)

Selection strategies vary in their characteristics:

Selection pressure: How strongly the algorithm favors high-fitness individuals
Diversity preservation: How well the strategy maintains population diversity
Computational complexity: Efficiency of the selection process
Scalability: Performance with different population sizes

Common selection patterns:

Exploitative strategies: High selection pressure (large tournaments, elitism)
Explorative strategies: Low selection pressure (small tournaments, random)
Balanced strategies: Moderate pressure with diversity preservation
Adaptive strategies: Selection pressure that changes during evolution

Selection policy design considerations:

Problem characteristics: Multimodal vs unimodal, deceptive problems
Population size: Small populations may need lower selection pressure
Fitness landscape: Rugged landscapes benefit from diversity preservation
Convergence requirements: Balance between speed and solution quality

Example usage in genetic algorithm configuration:


 // Tournament selection with moderate pressure
 SelectionPolicy tournament = Tournament.of(3);
 
 // Roulette wheel for fitness-proportionate selection
 SelectionPolicy roulette = RouletteWheel.build();
 
 // Multi-objective selection with double tournament
 SelectionPolicy doubleTournament = DoubleTournament.of(
     firstCriteria: 3,
     secondCriteria: 2
 );
 
 // Combined selection strategies
 SelectionPolicy multiSelection = MultiSelections.of(
     Tuple.of(0.7, tournament),
     Tuple.of(0.3, roulette)
 );
 
 // Use in EA configuration
 EAConfiguration<Double> config = EAConfigurationBuilder.<Double>builder()
     .chromosomeSpecs(chromosomeSpec)
     .parentSelectionPolicy(tournament)
     .build();

See Also:

Interface SelectionPolicy