Book Description
Genetic Algorithms (GAs) are nature-inspired optimization tools that help AI systems adapt, improve, and solve complex problems efficiently. Ultimate Genetic Algorithms with Python explains elaborately the fundamentals of GAs to practical, Python-based implementation, using PyGAD and DEAP.

The book starts with a solid foundation, explaining how evolutionary principles can be applied to optimization tasks, search problems, and model improvement.

You will also explore GA applications across multiple AI domains: optimizing machine learning workflows, evolving neural network architectures in deep learning, enhancing feature selection in NLP, improving performance in computer vision, and guiding exploration strategies in reinforcement learning. Each application chapter includes step-by-step coding examples, performance comparisons, and tuning techniques.

What’s Inside This Book:

• Step-by-step guidance for genetic algorithms Python hands-on projects
• Introduction to evolutionary computation for machine learning and AI optimization
• Detailed walkthroughs on using leading frameworks for AI optimization in Python
• Strategies for metaheuristic optimization in deep learning and computer vision applications
• Applied techniques for adaptive machine learning with genetic algorithms
• Evolutionary algorithms for feature selection in NLP and generative AI solutions
• Advanced topics including reinforcement learning with evolutionary computation and algorithm tuning
• Real-world projects and case studies for multi-agent genetic algorithm applications
• Methods for neural network architecture evolution and intelligent agent-based systems

Who Should Read This Book?

• Students, developers, and data scientists eager to learn evolutionary algorithms Python
• Professionals seeking machine learning optimization techniques for adaptive AI systems
• Enthusiasts interested in hands-on projects with computer vision, NLP, and generative AI optimization
• Researchers exploring multi-agent AI intelligence or algorithm tuning strategies

Your Roadmap to Intelligent AI Solutions:

• Build, optimize, and deploy real-world evolutionary projects using Python genetic algorithms
• Gain confidence with real-world algorithm tuning strategies and AI optimization solutions
• Master deep learning, reinforcement learning, and intelligent agent-based system techniques

Start building powerful and adaptive AI systems today!

Table of Contents
1. Introduction to Genetic Algorithms
2. Fundamentals of Genetic Algorithms
3. Overview of Genetic Algorithm Libraries
4. Genetic Algorithms and Their Applications
5. Foundation of Evolutionary Algorithms
6. Advanced Evolutionary Algorithms
7. Metaheuristic Optimization Algorithms
8. Application of Evolutionary Algo (GAs) and Generative Agentic AI
9. Applying Genetic Algorithm to Machine Learning
10. Applying Deep Learning to Genetic Algorithm
11. Applying Computer Vision Application to Genetic Algorithms
12. Applying NLP to Genetic Algorithms
13. Applying Reinforcement Learning to Genetic Algorithms
14. The Future of Genetic Algorithms
Index