Evolutionary Algorithms API

API reference for evolutionary algorithms in opt.evolutionary.

Module Overview

from opt.evolutionary import (
    GeneticAlgorithm,
    DifferentialEvolution,
    CMAES,
    CulturalAlgorithm,
    ImperialistCompetitive,
)

Common Interface

All evolutionary algorithms inherit from AbstractOptimizer:

class EvolutionaryAlgorithm(AbstractOptimizer):
    def __init__(
        self,
        func: Callable,
        lower_bound: float,
        upper_bound: float,
        dim: int,
        max_iter: int,
        population_size: int = 50,
        **kwargs
    ):
        pass

    def search(self) -> tuple[np.ndarray, float]:
        pass

Available Algorithms

• GeneticAlgorithm - Classic GA with crossover and mutation
• DifferentialEvolution - Vector-based evolution strategy
• CMAES - Covariance Matrix Adaptation ES
• CulturalAlgorithm - Dual inheritance system
• ImperialistCompetitive - Socio-political optimization

Example Usage

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,
    crossover_rate=0.8,
    mutation_rate=0.1
)
solution, fitness = ga.search()

# Differential Evolution
de = DifferentialEvolution(
    func=rastrigin,
    lower_bound=-5.12,
    upper_bound=5.12,
    dim=10,
    max_iter=100,
    population_size=50,
    F=0.8,  # Differential weight
    CR=0.9  # Crossover probability
)
solution, fitness = de.search()

See Also

• Evolutionary Algorithms - Algorithm details