📱 eBook en inglés ULTIMATE GENETIC ALGORITHMS WITH PYTHON: BUILD INTELLIGENT AND ADAPTIVE AI SYSTEMS WITH GENETIC ALGORITHMS IN PYTHON FOR MACHINE LEARNING, DEEP LEARNING, AND MULTI-AGENT DOMAINS

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.