Ultimate Genetic Algorithms with Python
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Harness Genetic Algorithms to Build the Next Generation of Adaptive AI. Key Features ¿ Step-by-step tutorials on Genetic Algorithms, using PyGAD and DEAP. ¿ Real-world Genetic Algorithm applications in ML, DL, NLP, CV, and RL. ¿ Advanced coverage of evolutionary and metaheuristic algorithms. ¿ Integration of Genetic Algorithms with generative and agent-based AI systems. 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 ...
Harness Genetic Algorithms to Build the Next Generation of Adaptive AI. Key Features ¿ Step-by-step tutorials on Genetic Algorithms, using PyGAD and DEAP. ¿ Real-world Genetic Algorithm applications in ML, DL, NLP, CV, and RL. ¿ Advanced coverage of evolutionary and metaheuristic algorithms. ¿ Integration of Genetic Algorithms with generative and agent-based AI systems. 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. The later sections focus on advanced metaheuristics, swarm intelligence, and integrating GAs with generative and agent-based AI systems. You will also learn how to design self-evolving, multi-agent frameworks, leverage swarm-based methods, and connect GAs to next-gen AI architectures such as Model Context Protocols (MCP). What you will learn ¿ Master the fundamentals and components of Genetic Algorithms. ¿ Implement GAs in Python, using PyGAD, DEAP, and PyTorch. ¿ Apply GAs for optimization, feature selection, and neural architecture search. ¿ Enhance AI workflows in ML, DL, NLP, CV, and RL with GAs. ¿ Explore metaheuristic and swarm-based algorithms for complex problem-solving. ¿ Integrate GAs into generative, multi-agent, and self-evolving AI systems. 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 About the Authors Indrajit Kar is a distinguished AI thought leader, innovator, and author with over 21 years of experience driving transformative AI-led products and platforms across industries. He has led high-impact teams delivering end-to-end solutions in Artificial Intelligence, Machine Learning, Generative AI, and Data Science-guiding projects from design to deployment and scaling. Zonunfeli Ralte is a pioneering AI leader, entrepreneur, and researcher with over 16 years of experience in Analytics and AI. As the founder of Northeast India's first AI company, she has positioned her organization at the forefront of applied AI, earning recognition as one of the most influential voices in both regional and global AI landscapes.
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