We investigate the use of optimization and data mining techniques for calibrating the input parameters to a discrete event simulation code. In the context of a breast-cancer epidemiology model we show how a hierarchical classifier can accurately predict those parameters that ensure the simulation replicates benchmark data within 95 % confidence intervals. We formulate an optimization model that evaluates solutions based on an integer valued score function. The scores are determined from a simulation run (and are therefore subject to stochastic variations), and are expensive to calculate. The Wisconsin Breast Cancer Epidemiology Simulation uses detailed individualwoman level discrete event simulation of four processes (breast cancer natural history, detection, treatment and non-breast cancer mortality among US women) to replicate breast cancer incidence rates according to the Surveillance, Epidemiology, and End Results (SEER) Program data from 1975 to 2000. Incidence rates are calculated for four different stages of tumor growth, namely in-situ, localized, regional and distant; these correspond to increasing size and/or progression of th
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