ix-evolution

name	ix-evolution
description	Evolutionary optimization — genetic algorithm, differential evolution
disable-model-invocation	true

Evolutionary Optimization

Population-based global optimization on benchmark functions.

When to Use

When the user needs gradient-free global optimization, wants to compare GA vs DE, or is working with non-convex objective functions.

Capabilities

• Genetic algorithm — Tournament selection, crossover, mutation
• Differential evolution — DE/rand/1/bin strategy
• Benchmark functions — Sphere, Rosenbrock, Rastrigin

Key Concepts

• GA: good for discrete/mixed problems, uses crossover + mutation
• DE: excellent for continuous optimization, uses vector differences
• Both are population-based and gradient-free

Programmatic Usage

use ix_evolution::genetic::GeneticAlgorithm;
use ix_evolution::differential::DifferentialEvolution;

MCP Tool

Tool name: ix_evolution Parameters: algorithm (genetic/differential), function, dimensions, generations