ART2 based classification of sparse high dimensional parameter sets for a simulation parameter selection assistant

This paper presents the design and creation of a simulation parameter selection assistant (SPSA) that helps modeling researchers choose meaningful values for their complex simulations, and encourages collaboration between teams searching through high dimensional parameter spaces. Proposed simulation parameters are compared to past runs using adaptive resonance theory to measure similarity with the goals of preventing repetitive exploitations of parameters and of encouraging the exploration of new regions of the parameter space. The assistant was designed to be used as part of a high performance animal disease spread simulator but is general and modular enough to be easily adapted to other simulation and search domains.