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

Paul G. Allen was an investor and philanthropist. He created and advanced world-class projects and high-impact initiatives that changed and improved the way people live, learn, work, and experience the world through arts, education, entertainment, sports, business, and technology. He cofounded Microsoft with Bill Gates in 1975 and remained the company's chief technologist until he left Microsoft in 1983. Allen founded Vulcan Inc. in 1986 and the Experience Music Project, now the Museum of Pop Culture, in 2000. With lifetime giving of about $1 billion, Allen was named one of the top philanthropists in America.