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Intellectics Group: Technical Report 98-01

Evaluating Las Vegas Algorithms: Pitfalls and Remedies

Holger Hoos and Thomas Stützle

Stochastic search algorithms are among the most sucessful approaches for solving hard combinatorial problems arising in applications from AI and other domains. A large class of stochastic search approaches can be cast into the framework of Las Vegas Algorithms (LVAs). Because the run-time behavior of LVAs is characterized by random variables, the detailed knowledge of run-time distributions provides important information for the analysis of these algorithms. In this paper we propose a novel methodology for evaluating the performance of LVAs, based on the identification of empirical run-time distributions. We exemplify our approach by applying it to Stochastic Local Search (SLS) algorithms for the satisfiability problem (SAT) in propositional logic. We point out pitfalls arising from the use of improper empirical methods and discuss the benefits of the proposed methodology for evaluating and comparing LVAs.

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