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Hands-On Genetic Algorithms with Python

You're reading from  Hands-On Genetic Algorithms with Python

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838557744
Pages 346 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Eyal Wirsansky Eyal Wirsansky
Profile icon Eyal Wirsansky
LinkedIn
Eyal Wirsansky is a senior data scientist, an experienced software engineer, a technology community leader, and an artificial intelligence researcher. Eyal began his software engineering career over twenty-five years ago as a pioneer in the field of Voice over IP. He currently works as a member of the data platform team at Gradle, Inc. During his graduate studies, he focused his research on genetic algorithms and neural networks. A notable result of this research is a novel supervised machine learning algorithm that integrates both approaches. In addition to his professional roles, Eyal serves as an adjunct professor at Jacksonville University, where he teaches a class on artificial intelligence. He also leads both the Jacksonville, Florida Java User Group and the Artificial Intelligence for Enterprise virtual user group, and authors the developer-focused artificial intelligence blog, ai4java.
See other products by Eyal Wirsansky

Table of Contents (18) Chapters

Preface 1. Section 1: The Basics of Genetic Algorithms
2. An Introduction to Genetic Algorithms 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

Technical requirements

In this chapter, we will be using Python 3 with the following supporting libraries:

  • deap
  • numpy
  • sklearn
  • gym—introduced in this chapter

The Gym environments that will be used in this chapter are MountainCar-v0 (https://gym.openai.com/envs/MountainCar-v0/) and CartPole-V1 (https://gym.openai.com/envs/CartPole-v1/).

The programs that will be used in this chapter can be found in this book's GitHub repository at https://github.com/PacktPublishing/Hands-On-Genetic-Algorithms-with-Python/tree/master/Chapter10.

Check out the following video to see the Code in Action:
http://bit.ly/2Oey7V0

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Authors (1)

Profile icon Eyal Wirsansky
LinkedIn
Eyal Wirsansky is a senior data scientist, an experienced software engineer, a technology community leader, and an artificial intelligence researcher. Eyal began his software engineering career over twenty-five years ago as a pioneer in the field of Voice over IP. He currently works as a member of the data platform team at Gradle, Inc. During his graduate studies, he focused his research on genetic algorithms and neural networks. A notable result of this research is a novel supervised machine learning algorithm that integrates both approaches. In addition to his professional roles, Eyal serves as an adjunct professor at Jacksonville University, where he teaches a class on artificial intelligence. He also leads both the Jacksonville, Florida Java User Group and the Artificial Intelligence for Enterprise virtual user group, and authors the developer-focused artificial intelligence blog, ai4java.
Read more
See other products by Eyal Wirsansky

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