Written by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms.
After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications.
By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.

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Spis treści

Hands-On Genetic Algorithms with Python. Apply genetic algorithms to solve real-world AI and machine learning problems - Second Edition eBook -- spis treści

• 1. An Introduction to Genetic Algorithms
• 2. Understanding the Key Components of Genetic Algorithms
• 3. Using the DEAP Framework
• 4. Combinatorial Optimization
• 5. Constraint Satisfaction
• 6. Optimizing Continuous Functions
• 7. Enhancing Machine Learning Models Using Feature Selection
• 8. Hyperparameter Tuning Machine Learning Models
• 9. Architecture Optimization of Deep Learning Networks
• 10. Reinforcement Learning with Genetic Algorithms
• 11. Natural Language Processing
• 12. Explainable AI and Counterfactuals
• 13. Speeding Up Genetic Algorithms with Concurrency
• 14. Harnessing the Cloud
• 15. Genetic Image Reconstruction
• 16. Other Evolutionary and Bio-Inspired Computation Techniques