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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
2. Chapter 1: An Introduction to Genetic Algorithms FREE CHAPTER 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 MountainCar environment

The MountainCar-v0 environment simulates a car on a one-dimensional track, situated between two hills. The simulation starts with the car placed between the hills, as shown in the following rendered output:

Figure 10.2: The MountainCar simulation – the starting point

Figure 10.2: The MountainCar simulation – the starting point

The goal is to get the car to climb up the taller hill – the one on the right – and ultimately hit the flag:

Figure 10.3: The MountainCar simulation – the car climbing the hill on the right

Figure 10.3: The MountainCar simulation – the car climbing the hill on the right

The simulation is set up with the car’s engine being too weak to directly climb the taller hill. The only way to reach the goal is to drive the car back and forth until enough momentum is built for climbing. Climbing the left hill can help to achieve this goal, as reaching the left peak will bounce the car back to the right, as shown in the following screenshot:

Figure 10.4: The MountainCar simulation – the car bouncing off the hill on the left

Figure 10.4: The...

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