Classification-Based Global Search: an Application to a Simulation for Breast Cancer

In simulation-based optimization, we seek the optimal parameter settings that minimize or maximize certain performance measures of the simulation system. In this paper, we use a two-phase approach to calibrate simulation parameters using classification tools. This classification-based method is used in Phase I to facilitate the global search process and it is followed by local optimization in Phase II. By learning knowledge from existing data the approach identifies potentially high-quality parameter settings. We present an example of its use on a Wisconsin breast cancer simulation.