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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 fitness function with the novelty score

Now we have defined the basic principles behind the NS method, we need to find a way to integrate it into the definition of the fitness function that will be used to guide the neuroevolution process. In other words, we need to define the novelty metric that can capture the amount of novelty that is introduced by a particular solver agent during the evolutionary process. There are several characteristics that can be used as novelty metrics for a solver agent:

  • The novelty of the solver genotype structure—the structural novelty
  • The stepping stones found in the search space of the solution—the behavioral novelty

Our primary interest in this chapter is to create a successful maze navigator agent. To successfully navigate through the maze, the agent must pay equal attention to most places in the maze. Such behavior can be achieved...

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