Ultimate Genetic Algorithms with Python

Harness Genetic Algorithms to Build the Next Generation of Adaptive AI.

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.

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