Genetic Algorithm: Table of Contents and Various Example Cases

This document provides a detailed directory structure of genetic algorithms, including various example cases with code implementations. For beginners, the content is presented in an easily understandable format with practical Python/Matlab code snippets demonstrating key genetic algorithm operations such as population initialization, fitness calculation, selection, crossover, and mutation.

Additionally, we include comprehensive explanations and annotated examples to help readers better understand the fundamental principles and real-world applications of genetic algorithms. Each example features algorithm walkthroughs and important function descriptions like roulette wheel selection, single-point crossover, and Gaussian mutation operations.

The document also contains practical exercises with solution frameworks to help readers consolidate their knowledge, including optimization problems and parameter tuning challenges. We hope this resource assists beginners in quickly mastering genetic algorithms and inspires continued interest in evolutionary computation techniques.