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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

Maze simulation environment

The environment for the maze simulation consists of three major components that are implemented as separate Python classes:

  • Agent: The class that holds information related to the maze navigator agent that is used by simulation (see the agent.py file for the implementation details).
  • AgentRecordStore: The class that manages the storage of records relating to evaluations of all the solver agents during the evolutionary process. The collected records can be used to analyze the evolutionary process after its completion (see the agent.py file for the implementation details).
  • MazeEnvironment: The class that contains information about the maze simulation environment. This class also provides methods that manage the simulation environment, control the position of a solver agent, perform collision detection, and generate the input data for sensors of the agent...
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