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Hands-On Genetic Algorithms with Python

You're reading from   Hands-On Genetic Algorithms with Python Apply genetic algorithms to solve real-world AI and machine learning problems

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781805123798
Length 418 pages
Edition 2nd 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 (24) Chapters Close

Preface 1. Part 1: The Basics of Genetic Algorithms FREE CHAPTER
2. Chapter 1: An Introduction to Genetic Algorithms 3. Chapter 2: Understanding the Key Components of Genetic Algorithms 4. Part 2: Solving Problems with Genetic Algorithms
5. Chapter 3: Using the DEAP Framework 6. Chapter 4: Combinatorial Optimization 7. Chapter 5: Constraint Satisfaction 8. Chapter 6: Optimizing Continuous Functions 9. Part 3: Artificial Intelligence Applications of Genetic Algorithms
10. Chapter 7: Enhancing Machine Learning Models Using Feature Selection 11. Chapter 8: Hyperparameter Tuning of Machine Learning Models 12. Chapter 9: Architecture Optimization of Deep Learning Networks 13. Chapter 10: Reinforcement Learning with Genetic Algorithms 14. Chapter 11: Natural Language Processing 15. Chapter 12: Explainable AI, Causality, and Counterfactuals with Genetic Algorithms 16. Part 4: Enhancing Performance with Concurrency and Cloud Strategies
17. Chapter 13: Accelerating Genetic Algorithms – the Power of Concurrency 18. Chapter 14: Beyond Local Resources – Scaling Genetic Algorithms in the Cloud 19. Part 5: Related Technologies
20. Chapter 15: Evolutionary Image Reconstruction with Genetic Algorithms 21. Chapter 16: Other Evolutionary and Bio-Inspired Computation Techniques 22. Index 23. Other Books You May Enjoy

Gymnasium

Gymnasium (https://gymnasium.farama.org/) – a fork and the official successor of OpenAI Gym – is an open source library that was written to allow access to a standardized set of reinforcement learning tasks. It provides a toolkit that can be used to compare and develop reinforcement learning algorithms.

Gymnasium consists of a collection of environments, all presenting a common interface called env. This interface decouples the various environments from the agents, which can be implemented in any way we like – the only requirement from the agent is that it can interact with the environment(s) via the env interface. This will be described in the next subsection.

The basic package, gymnasium, provides access to several environments and can be installed as follows:

pip install gymnasium

To allow us to render and animate our test environments, the PyGame library needs to be installed as well. This can be done using the following command:

pip...
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