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). Thus, by the end of the book, you will have developed all the skills to design, implement, and scale GA-driven solutions for real-world AI challenges. Hence, evolve your AI solutions—start building with Genetic Algorithms today!