December 2019
Intermediate to advanced
368 pages
11h 10m
English
Content preview from Hands-On Neuroevolution with Python
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Hypercube-Based NEAT for Visual Discrimination
In this chapter, you will learn about the main concepts behind a hypercube-based NEAT algorithm and about the main challenges it was designed to solve. We take a look at the problems that arise when attempting to use direct genome encoding with large-scale artificial neural networks (ANN) and how they can be solved with the introduction of an indirect genome encoding scheme. You will learn how a Compositional Pattern Producing Network (CPPN) can be used to store genome encoding information with an extra-high compression rate and how CPPNs are employed by the HyperNEAT algorithm. Finally, you will work with practical examples that demonstrate the power of the HyperNEAT algorithm.
In this chapter, ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.