A Dynamic Restart Strategy for Randomized BT Search

Local search (LS) and multi-agent-based search (ERA [1]) are stochastic and incomplete procedures for solving a Constraint Satisfaction Problem (CSP). Their performance is seriously undermined by local optima and deadlocks, respectively. Although complete, backtrack (BT) search suffers from thrashing and a high degree of unpredictability in its run-time even within the same problem domain. Further, when the problem is large, the completeness of BT cannot be guaranteed in practice. Gomes et al. [2] proposed to use randomization and rapid restarts (RRR) to overcome the heavy tail behavior of BT. RRR 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 [3] 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. Neither RRR nor RGR takes into account the intermediate results of search (i.e., across restarts). We propose an improved restart strategy, Randomization and Dynamic Geometric Restarts (RDGR), which dynamically adapts th