Computer Science – Neural and Evolutionary Computing
Scientific paper
2010-08-14
Computer Science
Neural and Evolutionary Computing
Scientific paper
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.
No associations
LandOfFree
Discover & eXplore Neural Network (DXNN) Platform, a Modular TWEANN does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Discover & eXplore Neural Network (DXNN) Platform, a Modular TWEANN, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Discover & eXplore Neural Network (DXNN) Platform, a Modular TWEANN will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-42274