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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

Indirect encoding of ANNs with CPPNs

In the previous chapters, you learned about the direct encoding of ANNs using the nature-inspired conception of a genotype that is mapped to the phenotype in a 1:1 ratio to represent the ANN topology. This mapping allows us to use advanced NEAT algorithm features such as an innovation number, which allows us to track when a particular mutation was introduced during the evolution. Each gene in the genome has a specific value of the innovation number, allowing fast and accurate crossover of parent genomes to produce offspring. While this feature introduces immense benefits and also reduces the computational costs needed to match the parent genomes during the recombination, the direct encoding used to encode the ANN topology of the phenotype has a significant drawback as it limits the size of the encoded ANN. The bigger the encoded ANN, the bigger...

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