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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 Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods
Overview of Neuroevolution Methods Python Libraries and Environment Setup Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems
Using NEAT for XOR Solver Optimization Pole-Balancing Experiments Autonomous Maze Navigation Novelty Search Optimization Method Section 3: Advanced Neuroevolution Methods
Hypercube-Based NEAT for Visual Discrimination ES-HyperNEAT and the Retina Problem Co-Evolution and the SAFE Method Deep Neuroevolution Section 4: Discussion and Concluding Remarks
Best Practices, Tips, and Tricks Concluding Remarks 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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