HyperNEAT represents a class of neuroevolutionary algorithms that captures some of the power of natural development with a ionally efficient high-level abstraction of development....
Jeff Clune, Benjamin E. Beckmann, Philip K. McKinl...
This work provides an analysis of using the evolutionary algorithm EPNet to create ensembles of artificial neural networks to solve a range of forecasting tasks. Several previous...
Abstract--Wireless interference is the major cause of degradation of capacity in 802.11 wireless networks. We present an approach to estimate the interference between nodes and lin...
We study the problem of navigating through a database of similar objects using comparisons. This problem is known to be strongly related to the small-world network design problem....
—This paper presents the advances of a research using a combination of recurrent and feed-forward neural networks for long term prediction of chaotic time series. It is known tha...