Genetic Algorithm Implementation for Function Optimization with GUI

This example presents a genetic algorithm solution for function optimization problems, packaged as a GUI-based application. The implementation features an intuitive interface that allows users to input objective functions and optimization parameters through interactive controls. Key algorithmic components include population initialization, fitness evaluation, selection operators (such as roulette wheel or tournament selection), crossover operations (with configurable rates), and mutation mechanisms. Users can control algorithm execution through interface buttons and sliders, while real-time visualization displays optimization progress and final results. The application employs MATLAB's GUI development framework, utilizing callback functions for event handling and data visualization components for result presentation. This approach enables users to apply genetic algorithms to function optimization without writing complex code or performing manual calculations, making advanced optimization techniques accessible through an interactive environment.