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The best performing algorithms for a particular oversubscribed schedulingapplication, Air Force Satellite Control Network (AFSCN) scheduling, appear tohave little in common. Yet, through careful experimentation and modeling ofperformance in real problem instances, we can relate characteristics of thebest algorithms to characteristics of the application. In particular, we findthat plateaus dominate the search spaces (thus favoring algorithms that makelarger changes to solutions) and that some randomization in exploration iscritical to good performance (due to the lack of gradient information on theplateaus). Based on our explanations of algorithm performance, we develop a newalgorithm that combines characteristics of the best performers; the newalgorithms performance is better than the previous best. We show how hypothesisdriven experimentation and search modeling can both explain algorithmperformance and motivate the design of a new algorithm.

Understanding Algorithm Performance on an Oversubscribed Scheduling Application