Computational and Mathematical Organization Theory

, Volume 17, Issue 3, pp 296–313 | Cite as

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

SI: BRIMS 2010


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.


Adaptive mesh Exploration Searching Parameter space Predictive analytics Volunteer computing High performance computing 


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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Lockheed Martin Systems Management Air Force Research LaboratoryWarfighter Readiness Research DivisionMesaUSA

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