Population Methods

Population-based optimizers maintain multiple candidate solutions that evolve together. They are excellent for complex, multi-modal optimization landscapes.

These methods are inspired by natural processes like evolution and swarm behavior, using populations of solutions that interact and improve over generations.

Particle Swarm Optimization

Particles “fly” through the search space, influenced by their own best position and the swarm’s best position.

from hyperactive.opt.gfo import ParticleSwarmOptimizer

optimizer = ParticleSwarmOptimizer(
    search_space=search_space,
    n_iter=100,
    experiment=objective,
)

Genetic Algorithm

Evolves a population using selection, crossover, and mutation inspired by biology.

from hyperactive.opt.gfo import GeneticAlgorithm

optimizer = GeneticAlgorithm(
    search_space=search_space,
    n_iter=100,
    experiment=objective,
)

Evolution Strategy

Similar to genetic algorithms but focused on real-valued optimization.

from hyperactive.opt.gfo import EvolutionStrategy

Differential Evolution

Uses vector differences to guide mutation. Excellent for continuous optimization.

from hyperactive.opt.gfo import DifferentialEvolution

Parallel Tempering

Runs multiple chains at different “temperatures” and exchanges information between them.

from hyperactive.opt.gfo import ParallelTempering

Spiral Optimization

Particles spiral toward the best solution found so far.

from hyperactive.opt.gfo import SpiralOptimization

When to Use Population Methods

Population-based optimizers are best suited for:

• Multi-modal landscapes: Problems with many local optima

• Complex interactions: When parameters interact in non-obvious ways

• Robustness: When you need reliable solutions across different runs

• Exploration: When thorough coverage of the search space is important

Consider using model-based methods if:

• Evaluations are very expensive (population methods need many evaluations)

• You want to leverage information from previous evaluations more efficiently