Real-Valued Genetic Algorithm Implementation

This document presents a detailed discussion of a real-encoded genetic algorithm program implemented in MATLAB. The program is designed to solve various optimization problems, including function optimization, prediction modeling, and classification tasks. Genetic algorithms represent optimization techniques based on principles of natural selection and genetic evolution, mimicking biological evolution processes through selection and crossover operations to improve solutions while maintaining diversity via genetic operators to avoid local optima. Real-valued encoding method represents continuous variables as chromosomes, making it particularly effective for optimization problems in continuous search spaces. The implementation typically includes key components such as population initialization using real-number vectors, fitness evaluation functions, tournament or roulette wheel selection mechanisms, arithmetic crossover operators for real-valued chromosomes, and mutation operations with Gaussian or uniform distributions. This real-encoded genetic algorithm has found extensive applications across multiple domains including machine learning, financial modeling, and industrial engineering. We will thoroughly examine the program's implementation approach, practical application scenarios, and optimization performance to provide comprehensive understanding of real-encoded genetic algorithms' fundamental principles and practical implementations.