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 models are routinely found in the modeling and simulation community. To alleviate the computational requirements, I have implemented two parallelized intelligent search and exploration algorithms: one based on adaptive mesh refinement and the other on regression trees. These algorithms were chosen because there is a dual interest in approaches that allow searching a parameter space for optimal values, as well as exploring the overall space in general. Both intelligent algorithms reduce computational costs at some expense to the quality of results, yet the regression tree approach was orders of magnitude faster than the other methodologies.
exploration parameter space searching adaptive mesh predictive analytics volunteer computing high performance computing