Genetic Algorithm for Solving Optimization Problems

This code collection primarily focuses on implementing genetic algorithm optimization solutions corresponding to Example 4-5 from Chapter 4 of "Mastering MATLAB Intelligent Algorithms." The implementation features vectorized operations where each function accepts vector inputs directly for efficient computation, utilizing MATLAB's matrix operations for performance optimization. Beyond the specific Example 4-5 application, these codes provide a flexible framework for solving various genetic algorithm-based optimization problems, including function minimization/maximization and parameter tuning. The implementation includes key genetic algorithm components such as population initialization, fitness evaluation, selection mechanisms, crossover operations, and mutation procedures. These codes are designed to help readers better understand and apply genetic algorithm concepts in practical scenarios. Before using this code, please ensure you have thoroughly studied Chapter 4 of the referenced book and have fundamental understanding of genetic algorithm principles, including chromosome representation, genetic operators, and convergence criteria.