December 2019
Intermediate to advanced
368 pages
11h 10m
English
Content preview from Hands-On Neuroevolution with Python
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ES-HyperNEAT algorithm
The ES-HyperNEAT algorithm starts with user-defined input nodes and elaborates on exploring connections from them and sending them to the newly expressed hidden nodes. The expression of outgoing connectivity patterns and hidden nodes placement within the substrate space is done using the quadtree information extraction method, which we described previously. The information extraction process is iteratively applied until the desired level of information expression density is achieved, or until no more information can be discovered in the hypercube. After that, the resulting network is connected to the user-defined output nodes by expressing the incoming connectivity patterns to the outputs. We use quadtree information ...
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