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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

Exercises

  1. Try to increase the value of the node_add_prob parameter and see what happens. Does the algorithm produce any number of hidden nodes, and if so, how many?
  2. Try to decrease/increase the compatibility_threshold value. What happens if you set it to 2.0 or 6.0? Can the algorithm find the solution in each case?
  3. Try to set the elitism value to zero in the DefaultReproduction section. See what happens. How long did the evolutionary process take to find an acceptable solution in this case?
  4. Set the survival_threshold value to 0.5 in the DefaultReproduction section. See how this affects speciation during evolution. Why does it?
  5. Increase the additional_num_runs and additional_steps values in order of magnitude to examine further how well the found control strategy is generalized. Is the algorithm still able to find a winning solution?
The last exercise will lead to an increase...
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