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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

Hyperparameter selection

The XOR experiment we will discuss in this chapter uses the NEAT-Python library as a framework. The NEAT-Python library defines a set of hyperparameters that are used to control the execution and performance of the NEAT algorithm. The configuration file is stored in a format similar to Windows .INI files; each section starts with a name in square brackets ([section]), followed by key-value pairs that are delimited by an equals sign (=).

In this section, we will discuss some hyperparameters of the NEAT-Python library that can be found in each section of the configuration file.

A full list of the hyperparameters in the NEAT-Python library can be found at https://neat-python.readthedocs.io/en/latest/config_file.html.

NEAT section

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