All Known Subinterfaces:: DeleteNLast, Elitism, GenerationalReplacement

All Known Implementing Classes:: ImmutableDeleteNLast, ImmutableElitism, ImmutableGenerationalReplacement, ImmutableSPEA2Replacement, SPEA2Replacement

public interface ReplacementStrategy

Marker interface for replacement strategy specifications in evolutionary algorithms.

ReplacementStrategy defines the contract for specifying how offspring are integrated into the population to form the next generation. Replacement strategies determine which individuals survive from one generation to the next, balancing exploration of new solutions with preservation of good existing solutions.

Replacement strategies are used by:

EA Configuration: To specify the survival strategy for generations
Replacement handlers: To create appropriate replacement implementations
Population management: To control population size and composition
Algorithm flow: To determine generational vs steady-state evolution

The framework provides several concrete replacement strategy implementations:

GenerationalReplacement: Complete replacement of parent generation
Elitism: Preserve best individuals while replacing others
DeleteNLast: Remove worst N individuals from combined population

Replacement strategies vary in their characteristics:

Selection pressure: How aggressively poor solutions are eliminated
Diversity preservation: How well genetic diversity is maintained
Convergence speed: How quickly the algorithm converges to solutions
Population dynamics: How population composition changes over time

Common replacement patterns:

Generational: All parents replaced by offspring each generation
Steady-state: Only a few individuals replaced per iteration
Elitist: Best individuals always survive to next generation
Tournament replacement: Compete offspring against existing individuals

Replacement strategy design considerations:

Population size: Maintain constant population size across generations
Selection pressure: Balance exploitation with exploration
Premature convergence: Prevent loss of genetic diversity too early
Solution preservation: Ensure good solutions are not lost

Interaction with other EA components:

Selection policies: Work together to control evolutionary pressure
Offspring ratio: Number of offspring affects replacement decisions
Population size: Determines how many individuals can be replaced
Fitness evaluation: Required for fitness-based replacement decisions

Example usage in genetic algorithm configuration:


 // Generational replacement (all parents replaced)
 ReplacementStrategy generational = GenerationalReplacement.build();
 
 // Elitist replacement preserving top 10% of individuals
 ReplacementStrategy elitist = Elitism.builder()
     .offspringSelectionPolicy(Tournament.of(3))
     .offspringRatio(0.9)  // Replace 90% of population
     .build();
 
 // Delete worst N individuals from combined population
 ReplacementStrategy deleteNLast = DeleteNLast.builder()
     .offspringSelectionPolicy(Tournament.of(2))
     .offspringRatio(0.5)  // Generate 50% offspring
     .build();
 
 // Use in EA configuration
 EAConfiguration<Double> config = EAConfigurationBuilder.<Double>builder()
     .chromosomeSpecs(chromosomeSpec)
     .parentSelectionPolicy(Tournament.of(3))
     .replacementStrategy(elitist)
     .build();

Performance implications:

Computational cost: Some strategies require sorting or complex selection
Memory usage: Combined populations may temporarily increase memory needs
Convergence rate: Affects how quickly the algorithm finds good solutions
Solution quality: Influences the final quality of evolved solutions

See Also:

Interface ReplacementStrategy