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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

NEAT algorithm overview

The method of NEAT for evolving complex ANNs was designed to reduce the dimensionality of the parameter search space through the gradual elaboration of the ANN's structure during evolution. The evolutionary process starts with a population of small, simple genomes (seeds) and gradually increases their complexity over generations.

The seed genomes have a very simple topology: only input, output, and bias neurons are expressed. No hidden nodes are introduced into the seed from the beginning to guarantee that the search for a solution starts in the lowest-dimensional parameter space (connection weights) possible. With each new generation, additional genes are introduced, expanding the solution search space by presenting a new dimension that previously did not exist. Thus, evolution begins by searching in a small space that can be easily optimized and...

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