A new class of search algorithms for adaptive computation

An adaptive probability state variable (PSV) parameter search algorithm Possessing long-term memory has been formulated to cope with systems that must avoid high performance-penalty operating regions. The information gained from all previous experiments is efficiently encoded in multivariate probability distribution functions (pdf's). This long-term memory capability enables the PSV algorithm effectively to avoid future experiments in high penalty regions. The systems considered are resource-limited, and catastrophic failure may occur if parameter values lying in high penalty regions are implemented. Those cases in which the high penalty regions are not known in advance were investigated. The PSV algorithm has the capability of adaptively learning the location and hypervolume of these regions as the search proceeds. The algorithm is explicitly guided in its internal strategies as a function of the remaining system resources and the updated probability distribution functions. Clustering analysis is used both in the discovery of new operating regions and for updating the pdf's, As a by-product of this research, clustering was also investigated as a presearch scheme. It is shown that this procedure has great promise as a means of assessing the complexity of an optimization problem. Experimental results are presented to demonstrate the utility of the adaptive PSV search algorithm.