arXiv:1008.2412cs.NE
Discover & eXplore Neural Network (DXNN) Platform, a Modular TWEANN
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Abstract
In this paper I present a novel type of Topology and Weight Evolving Artificial Neural Network (TWEANN) system called Modular Discover & eXplore Neural Network (DXNN). Modular DXNN utilizes a hierarchical/modular topology which allows for highly scalable and dynamically granular systems to evolve. Among the novel features discussed in this paper is a simple and database friendly encoding for hierarchical/modular NNs, a new selection method aimed at producing highly compact and fit individuals within the population, a "Targeted Tunning" system aimed at alleviating the curse of dimensionality, and a two phase based neuroevolutionary approach which yields high population diversity and removes the need for speciation algorithms. I will discuss DXNN's mutation operators which are aimed at improving its efficiency, expandability, and capabilities through a built in feature selection method that allows for the evolved system to expand, discover, and explore new sensors and actuators. Finally I will compare DXNN platform to other state of the art TWEANNs on a control task to demonstrate its superior ability to produce highly compact solutions faster than its competitors.
They seem to be similar to Gene Sher's TWEANNS - Topology and Weight Evolving Neural Networks - that I learned about in his 2012 book, "Handbook of Neuroevolution Through Erlang" (sure, it didn't pick up because Erlang ;)) Gene's specific implementation is DXNN (Discover and…
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