MATLAB Source Code for Genetic Algorithm with Elitism

In this article, the author shares MATLAB source code that implements a genetic algorithm with elitism. The code provides a comprehensive description of the entire genetic algorithm workflow, enabling readers to better understand its operational principles. The implementation enhances the basic genetic algorithm by introducing elitism, which substantially improves the original algorithm's performance. In the elitism-based genetic algorithm, the best individuals from each generation are preserved and directly passed to the next generation, thereby maintaining superior genetic material. This enhancement makes the genetic algorithm more effective for solving complex optimization problems. The code implementation typically includes functions for: - Population initialization with random or heuristic-based solutions - Fitness evaluation and ranking of individuals - Elite selection and preservation mechanisms - Crossover operations (single-point/multi-point) for genetic recombination - Mutation operators maintaining population diversity - Termination criteria based on convergence or generation count This resource serves as an excellent introductory material for learning genetic algorithms, demonstrating practical implementation techniques while explaining core concepts through commented MATLAB code. The elitism strategy prevents loss of optimal solutions and accelerates convergence, making it suitable for various engineering optimization applications.