MATLAB Code Implementation of Genetic Algorithm for Multi-Objective Optimization

This MATLAB-implemented genetic algorithm program provides solutions for multi-objective optimization problems. Genetic algorithms are heuristic search algorithms that simulate biological evolution processes to iteratively optimize problem solutions. The implementation includes key components such as population initialization with randomized chromosome encoding, tournament selection for parent choosing, single-point or multi-point crossover operations for genetic recombination, and mutation operators with adjustable probability rates. The algorithm incorporates Pareto dominance principles for handling multiple objectives and can be applied across various domains including engineering optimization, data mining, and machine learning. Through this MATLAB-based genetic algorithm implementation, users can efficiently solve complex multi-object optimization challenges with improved solution accuracy and computational efficiency. The program structure typically includes main functions for population management, fitness evaluation using weighted sum or ranking methods, elitism preservation, and convergence monitoring through generation-based termination criteria.