Population-Based Algorithms

Population-based algorithms maintain multiple candidate solutions simultaneously, using mechanisms inspired by natural evolution or swarm behavior to explore the search space efficiently.

Algorithm Examples

Algorithm	Example
Particle Swarm	particle_swarm_example.py
Genetic Algorithm	genetic_algorithm_example.py
Evolution Strategy	evolution_strategy_example.py
Differential Evolution	differential_evolution_example.py
Parallel Tempering	parallel_tempering_example.py
Spiral Optimization	spiral_optimization_example.py

When to Use Population-Based Methods

Population-based algorithms are best suited for:

• Complex, multimodal landscapes with many local optima

• Parallelizable evaluations where multiple candidates can be evaluated simultaneously

• Robust optimization where diversity helps avoid premature convergence

• Large search spaces requiring extensive exploration

See Optimizers for detailed algorithm descriptions.