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Hands-On Genetic Algorithms with Python

You're reading from   Hands-On Genetic Algorithms with Python Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838557744
Length 346 pages
Edition 1st Edition
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Author (1):
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Eyal Wirsansky Eyal Wirsansky
Author Profile Icon Eyal Wirsansky
Eyal Wirsansky
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Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics of Genetic Algorithms
2. An Introduction to Genetic Algorithms FREE CHAPTER 3. Understanding the Key Components of Genetic Algorithms 4. Section 2: Solving Problems with Genetic Algorithms
5. Using the DEAP Framework 6. Combinatorial Optimization 7. Constraint Satisfaction 8. Optimizing Continuous Functions 9. Section 3: Artificial Intelligence Applications of Genetic Algorithms
10. Enhancing Machine Learning Models Using Feature Selection 11. Hyperparameter Tuning of Machine Learning Models 12. Architecture Optimization of Deep Learning Networks 13. Reinforcement Learning with Genetic Algorithms 14. Section 4: Related Technologies
15. Genetic Image Reconstruction 16. Other Evolutionary and Bio-Inspired Computation Techniques 17. Other Books You May Enjoy

Who this book is for

This book was written to help software developers, data scientists, and AI enthusiasts interested in harnessing genetic algorithms to carry out tasks involving learning, searching, and optimization in their applications, as well as enhancing the performance and accuracy of their existing intelligent applications.

This book is also intended for anyone who is tasked with real-life, hard-to-solve problems where traditional algorithms are not useful, or fail to provide adequate results within a practical amount of time. The book demonstrates how genetic algorithms can be used as a powerful, yet simple, approach to solving a variety of complex problems.

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