[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzk5ODA","title","Genetic Algorithms From Theory to Real Projects","state","published","contactAuthorMethod","email","aboutText","

Genetic Algorithms From Theory to Real Projects is a practical, project-based guide to understanding and implementing genetic algorithms with .NET and C#.

The book starts from the fundamentals: genes, chromosomes, populations, fitness functions, selection, crossover, mutation, elitism, and parameter tuning. It then builds a complete genetic algorithm from scratch before refactoring the logic into a reusable GA.Core library.

Instead of staying at the theory level, the book focuses on real working applications. You will see genetic algorithms applied to classic optimization problems, routing, scheduling, electronics, machine learning, neuroevolution, and game AI.

The examples include phrase evolution, 8-Queens, Knapsack, Traveling Salesman Problem, Sudoku, Timetabling, Rectangle Packing, Graph Coloring, Vehicle Routing, Maze Solving, Analog RC Filter design, Op-Amp gain optimization, Rastrigin minimization, Gear Train optimization, Image Approximation, Rubik's Cube solving, ML.NET hyperparameter tuning, Feature Selection, Lunar Lander policy search, Neural Architecture Search, and Snake neuroevolution.

All source code is implemented in C# and .NET, with many visual examples built using WPF so that readers can observe how populations evolve over time.

This book is written for developers, students, engineers, and researchers who want to move beyond abstract explanations and learn genetic algorithms by building real projects.

Watch the Demo Videos

You can watch short demo videos for many of the visual projects on YouTube:

https://www.youtube.com/@keremaktug9822/playlists

Source Code

All companion source code is available on GitHub:

https://github.com/keremaktug/Genetic-Algorithms-From-Theory-to-Real-Projects

Also Available on Gumroad

You can also get the book on Gumroad:

https://keremaktug.gumroad.com/l/iygzyk

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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