Advanced Selection Strategies¶

Implementing sophisticated solution selection mechanisms for multi-objective optimization.

Note

📝 Under Development: This section is planned for future development. The Distance-Based Archive Tutorial provides a concrete example of advanced selection strategies.

Overview¶

Selection strategies determine which solutions to maintain in archives and populations during optimization. Advanced strategies can significantly improve optimization performance.

Planned Topics¶

Distance-Based Selection¶

Multi-Objective Distance Metrics: Beyond Euclidean distance
Adaptive Distance Measures: Context-aware distance calculations
Normalized vs Raw Objectives: When and how to normalize

Diversity Maintenance¶

Crowding Distance Variations: Improvements to standard crowding distance
Hypervolume-Based Selection: Using hypervolume for selection
Reference Point Methods: Selection with user preferences

Performance Optimization¶

Incremental Updates: Efficient recomputation strategies
Approximate Methods: Trading accuracy for speed
Parallel Selection: Distributed selection algorithms

Hybrid Approaches¶

Multi-Criteria Selection: Combining multiple selection criteria
Adaptive Strategies: Changing selection during optimization
Problem-Specific Methods: Tailored selection for specific domains

Examples to be Covered¶

Knee Point Selection: Identifying solutions at trade-off knees
User-Preference Integration: Interactive selection strategies
Constraint-Aware Selection: Handling feasibility in selection
Dynamic Population Sizing: Adaptive archive and population sizes

Current Implementation¶

The Distance-Based Archive Tutorial demonstrates several advanced concepts:

Adaptive strategy selection based on problem dimensionality
Robust normalization handling edge cases
Integration of crowding distance and distance-based methods
Memory-efficient implementation patterns

Table Of Contents

Contents