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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

Objective function for a double-pole balancing experiment

The objective function for this problem is similar to the objective function defined earlier for the single-pole balancing problem. It is given by the following equations:

In these equations, is the expected number of time steps specified in the configuration of the experiment (100,000), and is the actual number of time steps during which the controller was able to maintain a stable state of the pole balancer within the specified limits.

We use logarithmic scales because most of the trials fail in the first several 100 steps, but we are testing against 100,000 steps. With a logarithmic scale, we have a better distribution of fitness scores, even compared with a small number of steps in failed trials.

The first of the preceding equations defines the loss, which is in the [0,1] range, and the second is a fitness score...

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