Cognitive model exploration and optimization: a new challenge for computational science

Parameter space exploration is a common problem tackled on large-scale computational resources. The most common technique, a full combinatorial mesh, is robust but scales poorly to the computational demands of complex models with higher dimensional spaces such as those found in the cognitive and behavioral modeling community. To curtail the computational requirements, I have implemented two parallelized intelligent search and exploration algorithms, both of which are discussed and compared in this paper.