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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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Toc

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

The NS optimization method

The main idea behind NS is to reward the novelty of the produced solution rather than its progress to the final goal. This idea is inspired by natural evolution. When looking for a successful solution, it is not always obvious the exact steps that should be taken. Natural evolution continuously produces novel forms, with different phenotypes trying to exploit the surrounding environment and adapt to the changes. This has allowed an explosion of life forms on Earth and ignited qualitative leaps forward in the evolution of life. The same process allowed life forms to leave the sea and conquer the land. The extraordinary genesis of eukaryotes became the source of all higher life forms on the planet. All these are examples of rewarding novelty during evolution. At the same time, there is no clear objective or final goal in natural evolution.

As you learned...

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