An Empirical Study of a New Restart Strategy for Randomized Backtrack Search

We propose an improved restart strategy for randomized backtrack search and compare its performance to other search mechanisms in the context of solving a tight real-world resource allocation problem. The restart strategy proposed by Gomes et al. [1] requires the specification of a cutoff value determined from an overall profile of the cost of search for solving the problem. When no such profile is known, the cutoff value is chosen by trial-and-error. Walsh proposed the strategy Randomization and Geometric Restart (RGR), which does not rely on a cost profile but determines the cutoff value as a function of a constant parameter and the number of variables in the problem [2]. Unlike these strategies, which have fixed restart schedules, our technique (RDGR) dynamically adapts the value of the cutoff parameter to the results of the search process. We empirically evaluate the performance of RDGR by comparing it against a number of heuristic and stochastic search techniques, including RGR, using the cumulative distribution of the solutions. We compare the performance of RGR and RDGR over different run-time durations, different values of the cutoff, and for different problem types (i.e., a real-world resource allocation problem and randomlygenerated binary constraint satisfaction problems). We show that distinguishing between solvable and over-constrained problem instances in our real-world casestudy yields new insights on the relative performance of the search techniques tested. We propose to use this characterization as a basis for building new strategies of cooperative, hybrid search.