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Hands-On Neuroevolution with Python.

You're reading from  Hands-On Neuroevolution with Python.

Product type Book
Published in Dec 2019
Publisher Packt
ISBN-13 9781838824914
Pages 368 pages
Edition 1st Edition
Languages
Author (1):
Iaroslav Omelianenko Iaroslav Omelianenko
Profile icon Iaroslav Omelianenko

Table of Contents (18) Chapters

Preface 1. Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods
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

Visual discrimination experiment setup

In our experiment, during the training of the discriminator ANN, we use the resolution of the visual and target fields fixed at 11 x 11. Thus, the connective CPPN must learn the correct connectivity pattern between the 121 inputs of the visual field and the 121 outputs of the target fields, which results in a total of 14,641 potential connection weights.

The following diagram shows the scheme of the substrate for the discriminator ANN:

The state-space sandwich substrate of the discriminator ANN

The discriminator ANN shown in the diagram has two layers with nodes forming one two-dimensional planar grid per layer. The connective CPPN draws the connectivity patterns by connecting nodes from one layer to another.

At each generation of the evolution, each individual in the population (the genome encoding the CPPN) is evaluated for its ability...

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