On the Potential of Automatic Algorithm Configuration

Design and implementation of efficient and robust algorithms are core topics of computer science and operations research, and the determination of appropriate values for free algorithm parameters is a challenging and tedious task in the design of effective algorithms for hard problems. Such parameters include categorical choices (e.g., neighborhood structure in local search or variable/value ordering heuristics in tree search), as well as numerical parameters (e.g., noise or restart timing). In practice, tuning of these parameters is largely carried out manually by applying rules of thumb and crude heuristics, while more principled approaches are only rarely used. In this paper, we study some tuning scenarios in more detail and demonstrate the large potential of even very simple automatic algorithm configuration approaches.