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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

Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods

This section introduces core concepts of evolutionary computation and discusses particulars of neuroevolution-based algorithms and which Python libraries can be used to implement them. You will become familiar with the fundamentals of neuroevolution methods and will get practical recommendations on how to start your experiments. This section provides a basic introduction to the Anaconda package manager for Python as part of your environment setup.

This section comprises the following chapters:

  • Chapter 1, Overview of Neuroevolution Methods
  • Chapter 2, Python Libraries and Environment Setup
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Hands-On Neuroevolution with Python
Published in: Dec 2019
Publisher: Packt
ISBN-13: 9781838824914
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