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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

SAFE method

As the name suggests, the SAFE method is about the co-evolution of the solution and the fitness function, which guides the solution search optimization. The SAFE method is built around the commensalistic co-evolution strategy of two populations:

  • The population of potential solutions, which evolve to solve the problem at hand
  • The population of objective function candidates, which evolve to guide the evolution of the solution population

In this book, we have already discussed several search optimization strategies that can be used to guide the evolution of potential solution candidates. These strategies are objective-based fitness optimization and Novelty Search optimization. The former optimization strategy is perfect in situations when we have a plain fitness function landscape and can concentrate our optimization search on the ultimate goal. In this case, we can...

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