We investigate Monte Carlo Markov Chain (MCMC) procedures for the random sampling of some one-dimensional lattice paths with constraints, for various constraints. We will see that...
Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are speci...
Martin V. Butz, Martin Pelikan, Xavier Llorà...
A dynamically reconfigurable processor (DRP) is designed to achieve high area efficiency by switching reconfigurable data paths dynamically. Our DRP architecture has a stand alone...
Volunteer Computing (VC) is a paradigm that takes advantage of idle cycles from computing resources donated by volunteers and connected through the Internet to compute large-scale...
One challenge for research in constraint-based scheduling has been to produce scalable solution procedures under fairly general representational assumptions. Quite often, the comp...