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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

Solving the CartPole environment

The CartPole-v1 environment simulates a balancing act of a pole, hinged at its bottom to a cart, which moves left and right along a track. Balancing the pole upright is carried out by applying to the cart 1 unit of force – to the right or the left – at a time.

The pole, acting as a pendulum in this environment, starts upright within a small random angle, as shown in the following rendered output:

Figure 10.6: The CartPole simulation – the starting point

Figure 10.6: The CartPole simulation – the starting point

Our goal is to keep the pendulum from falling over to either side for as long as possible – that is, up to 500 time steps. For every time step that the pole remains upright, we get a reward of +1, so the maximum total reward is 500. The episode will end prematurely if one of the following occurs during the run:

  • The angle of the pole from the vertical position exceeds 15 degrees
  • The cart’s distance from the center exceeds...
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