- Preface
- About This Book
- Who Should Read This Book?
- Source Code and Implementation
- Code Structure
- Foundations
- Definition and History
- Usage Fields
- Biological Background
- Core Concepts
- Genes
- Chromosomes
- Population
- Selection
- Crossover
- Mutation
- Elitism
- How GA Works
- Genetic Algorithm Workflow
- Parameter Tuning
- From Scratch and GA.Core
- Evaluating a Phrase
- Evaluating a Phrase with GUI
- GA.Core Reusable Library
- Phrase With Core WPF
- Classic Optimization Projects
- Eight Queens
- Knapsack
- Traveling Salesman Problem
- Sudoku
- Timetabling
- Rectangle Packing
- Graph Coloring
- Logistics and Planning Projects
- Vehicle Routing
- Maze Solver
- Engineering and Continuous Search
- Analog RC Filter
- Analog Op-Amp Gain
- Rastrigin Function Minimization
- Gear Train Optimization
- Puzzle and Creative Search
- Image Approximation
- Rubik's Cube Solver
- Machine Learning Applications
- ML.NET Hyperparameter Optimization
- Random Forest Hyperparameter Optimization
- Feature Selection Console
- Neuroevolution and Policy Search
- Lunar Lander Policy Search
- Neural Architecture Search
- Snake Agent with Neuroevolution
- Index
Genetic Algorithms From Theory to Real Projects
A Practical .NET Guide to Building Genetic Algorithms from Scratch and Applying Them to Real-World Optimization Problems
This book is 100% completeLast updated on 2026-06-15
Learn genetic algorithms by building real projects in C# and .NET. This book takes you from a from-scratch implementation to a reusable GA.Core library, then applies it to optimization, routing, scheduling, electronics, machine learning, neuroevolution, and game AI problems.
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About the Book
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.
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About the Author
Kerem Aktuğ
Kerem Aktuğ is a software architect based in Türkiye, with professional experience in designing and building software systems using .NET and C#.
His work focuses on practical software architecture, algorithmic problem solving, optimization techniques, and building real-world applications that connect theory with implementation. He is especially interested in making complex technical topics easier to understand through hands-on projects, visual examples, and clean, reusable code.
In Genetic Algorithms From Theory to Real Projects, he combines his software engineering background with a project-based teaching approach. The book reflects his belief that algorithms are best learned by building, experimenting, visualizing, and applying them to real problems.
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