Basic Genetic Algorithm (GA) MATLAB Implementation

This article provides the MATLAB source code for a basic genetic algorithm (GA) implementation! The code structure allows for numerous improvements to develop more efficient genetic algorithm variants. Genetic algorithms simulate natural selection and genetic mechanisms to solve optimization problems, making them applicable across domains like machine learning, engineering optimization, and complex system design. The implementation demonstrates core GA components including population initialization, fitness evaluation, selection operators (roulette wheel/tournament), crossover operations (single-point/multi-point), and mutation mechanisms. Through this foundation, users can explore how GAs search solution spaces to find optimal solutions for complex problems. By studying and modifying this basic implementation, developers can better understand GA workflow principles and adapt the algorithm to specific real-world applications. The code structure facilitates experimentation with different selection strategies, crossover methods, and mutation rates to optimize performance for particular problem domains.