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...
In the context of classification problems, algorithms that generate multivariate trees are able to explore multiple representation languages by using decision tests based on a com...
This paper presents a general methodology for the efficient parallelization of existing data cube construction algorithms. We describe two different partitioning strategies, one f...
Frank K. H. A. Dehne, Todd Eavis, Susanne E. Hambr...
Fault Abstraction and Collapsing Framework for Asynchronous Circuits Philip P. Shirvani, Subhasish Mitra Center for Reliable Computing Stanford University Stanford, CA Jo C. Eberge...
Philip P. Shirvani, Subhasish Mitra, Jo C. Ebergen...
The underlying research topics and the architecture of the UBU team are briefly described. The aim of developing UBU is to subject a series of tools and procedures for agent decis...