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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 Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods
Overview of Neuroevolution Methods Python Libraries and Environment Setup Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems
Using NEAT for XOR Solver Optimization Pole-Balancing Experiments Autonomous Maze Navigation Novelty Search Optimization Method Section 3: Advanced Neuroevolution Methods
Hypercube-Based NEAT for Visual Discrimination ES-HyperNEAT and the Retina Problem Co-Evolution and the SAFE Method Deep Neuroevolution Section 4: Discussion and Concluding Remarks
Best Practices, Tips, and Tricks Concluding Remarks Other Books You May Enjoy

Selecting the optimal search optimization method

In this book, we have presented you with two basic search optimization methods: goal-oriented search and Novelty Search. The former method is more straightforward to implement and easier to understand. However, Novelty Search is handy in cases where the fitness function has a deceptive landscape with many local optima traps.

In the next section, we briefly discuss both methods to remind you of the details and to help you choose which one to use in a given situation. We start with goal-oriented search.

Goal-oriented search optimization

Goal-oriented search optimization is based on measuring the proximity of the solution to the ultimate goal. To calculate the average distance...

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