Evolutionary Algorithms

Optimization algorithms inspired by biological evolution and natural selection.

Overview

Evolutionary algorithms use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. They maintain a population of candidate solutions and evolve them over generations.

Available Algorithms

• Genetic Algorithm - Classic evolutionary algorithm
• Differential Evolution - Vector-based evolutionary strategy
• CMA-ES - Covariance Matrix Adaptation Evolution Strategy
• Cultural Algorithm - Dual inheritance system
• Imperialist Competitive Algorithm - Socio-politically inspired

Usage Example

from opt.evolutionary import GeneticAlgorithm, DifferentialEvolution
from opt.benchmark.functions import rastrigin

# Genetic Algorithm
ga = GeneticAlgorithm(
    func=rastrigin,
    lower_bound=-5.12,
    upper_bound=5.12,
    dim=10,
    max_iter=100,
    population_size=50
)
best_solution, best_fitness = ga.search()

See Also

• API Reference - Complete API documentation
• Swarm Intelligence - Related population-based methods