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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

Summary

In this chapter, we learned about four popular Python libraries that we can use for experiments in the field of neuroevolution. We discussed the strengths and weaknesses of each library that was presented, and reviewed the basic examples of using these libraries in Python. After that, we looked at how to set up the environment for Python-based experiments to avoid the side effects of having multiple versions of the same library in the Python path. We found that the best way to do this is to create isolated virtual environments for each Python project, and considered several popular solutions created by the open source community to help with this task. Finally, we introduced Anaconda Distribution, which includes, among other useful things, the package manager and an environment manager. For the rest of this book, we will use Anaconda to handle setting up the environment...

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