  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.
