Genetic Algorithm Optimization for Simple Functions

This example demonstrates genetic algorithm optimization for simple functions using MATLAB programming. Genetic algorithms are optimization techniques that mimic biological evolution processes to search for optimal solutions. As adaptive global search methods, they are particularly effective for solving complex optimization problems. In this implementation, the genetic algorithm operates through key evolutionary steps: fitness evaluation, selection, crossover, and mutation operations. The MATLAB Genetic Algorithm Toolbox provides built-in functions to streamline these processes. For fitness evaluation, the algorithm calculates objective function values for each chromosome in the population. Selection methods like roulette wheel or tournament selection choose parents for reproduction based on fitness scores. Crossover operations (typically single-point or uniform crossover) combine parent chromosomes to create offspring, while mutation introduces random changes to maintain genetic diversity. The algorithm iteratively improves solutions through generations, gradually converging toward optimal values. Key MATLAB functions employed include `ga` for main algorithm execution, fitness function definition using anonymous functions or separate files, and parameter configuration through options structures. This example provides hands-on experience in formulating optimization problems, setting algorithm parameters, and analyzing convergence behavior, establishing fundamental skills for solving real-world optimization challenges with genetic algorithms in MATLAB.