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Hands-On Neuroevolution with Python

You're reading from   Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838824914
Length 368 pages
Edition 1st Edition
Languages
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Author (1):
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Iaroslav Omelianenko Iaroslav Omelianenko
Author Profile Icon Iaroslav Omelianenko
Iaroslav Omelianenko
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods FREE CHAPTER
2. Overview of Neuroevolution Methods 3. Python Libraries and Environment Setup 4. Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems
5. Using NEAT for XOR Solver Optimization 6. Pole-Balancing Experiments 7. Autonomous Maze Navigation 8. Novelty Search Optimization Method 9. Section 3: Advanced Neuroevolution Methods
10. Hypercube-Based NEAT for Visual Discrimination 11. ES-HyperNEAT and the Retina Problem 12. Co-Evolution and the SAFE Method 13. Deep Neuroevolution 14. Section 4: Discussion and Concluding Remarks
15. Best Practices, Tips, and Tricks 16. Concluding Remarks 17. Other Books You May Enjoy

Using NEAT for XOR Solver Optimization

In this chapter, you will learn about one of the classic computer science experiments that demonstrates that the NEAT algorithm works and can create a proper network topology. In this chapter, you will get first-hand experience of writing an objective function to guide the XOR problem solver. You will also learn how to select the correct hyperparameters of the NEAT algorithm to assist with solving the XOR problem. This chapter aims to introduce you to the basic techniques of how to apply the NEAT algorithm to solve classic computer science problems.

After completing the experiment and exercises described in this chapter, you will have a solid understanding of the XOR experiment's particulars and get the practical skills you need to write the relevant Python source code using the NEAT-Python library. You will also gain experience in setting...

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