Solving Genetic Algorithm Problems with Custom MATLAB Implementation

When solving genetic algorithm problems using MATLAB, you can choose to avoid the built-in Genetic Algorithm Toolbox functions. Instead, you can manually implement each step of the genetic algorithm process including selection, crossover, and mutation operations. This approach provides deeper understanding of genetic algorithm principles and working mechanisms, while allowing custom optimization for specific problems. In your implementation, you would typically create functions for key operations: fitness evaluation to score individual solutions, selection methods like roulette wheel or tournament selection, crossover techniques such as single-point or uniform crossover, and mutation operators that introduce random changes. The main algorithm would involve initializing a population, iterating through generations while applying these operations, and maintaining elitism to preserve best solutions. Additionally, you can reference relevant literature and resources to learn more about genetic algorithm implementation methods and techniques. This hands-on approach enables better problem-solving capabilities with greater flexibility and control over the optimization process. You can implement termination conditions based on generation count or convergence criteria, and visualize the evolution progress through fitness value plots.