Excellent Genetic Algorithm Reference for Constrained Function Optimization

This text presents an outstanding genetic algorithm reference program specifically engineered for optimizing functions subject to constraints. The implementation demonstrates sophisticated constraint-handling techniques through penalty functions or specialized operators that maintain solution feasibility throughout evolution. The algorithm employs standard genetic operations including tournament selection, simulated binary crossover (SBX), and polynomial mutation, enhanced with constraint-aware modifications. Its architecture allows straightforward adaptation to specific problems through configurable parameters for population size, crossover rate, and mutation probability. This reference serves as a powerful tool for tackling complex optimization challenges in both academic research and engineering applications. The code structure facilitates easy modification of objective functions and constraint definitions through modular function interfaces. By implementing elitism and adaptive mutation strategies, the program significantly improves solution quality and convergence speed for constrained optimization problems. The constraint-handling component intelligently manages both equality and inequality constraints through gradient-based repair mechanisms or stochastic ranking techniques. This genetic algorithm reference provides substantial value across multiple domains by balancing exploration and exploitation while maintaining constraint satisfaction throughout the optimization process.