MATLAB-Based Genetic Algorithm for Function Optimization

This MATLAB-based genetic algorithm program for function optimization exhibits robust performance, implementing complete GA workflow including population initialization, fitness evaluation, tournament selection, simulated binary crossover, and polynomial mutation operations. The code contains extensively documented comments explaining chromosome encoding techniques, elitism preservation strategies, and termination criteria configuration. Users can efficiently solve complex optimization problems through adaptable parameters such as customizable fitness functions, adjustable population sizes (typically 30-100 individuals), and modifiable crossover rates (0.6-0.9) and mutation rates (0.01-0.1). The implementation demonstrates convergence monitoring through generation-based fitness progression plots, making it equally valuable for educational purposes and professional applications. The modular architecture allows straightforward customization of genetic operators and objective functions, significantly reducing development time while ensuring optimal solution quality through adaptive penalty methods for constraint handling.