MATLAB Genetic Algorithm Implementation with Code Examples

This article presents a practical MATLAB implementation of a genetic algorithm, providing a detailed walkthrough of how to construct a complete genetic algorithm case study. In this example, we will apply genetic algorithm techniques to solve a typical optimization problem. The implementation begins with an explanation of genetic algorithm fundamentals and procedural steps. We then define a specific optimization challenge and demonstrate how to use MATLAB's programming capabilities to achieve optimal solutions. Throughout the process, we break down each genetic algorithm component with corresponding code implementations: selection operations (using techniques like roulette wheel or tournament selection), crossover operations (implementing single-point or multi-point recombination), and mutation operations (applying controlled random modifications). Each MATLAB function includes explanations of key parameters and algorithmic approaches, such as population initialization using rand() or randi() functions, fitness evaluation through objective function calculation, and generation of new populations through genetic operators. This case study aims to provide clear understanding and practical application skills for genetic algorithm implementation in MATLAB environments!