Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors

Abstract Portfolio-based methods exploit the complementary strengths of a set of algorithms and—as evidenced in recent competitions—represent the state of the art for solving many NP-hard problems, including SAT. In this work, we argue that a state-of-the-art method for constructing portfolio-based algorithm selectors, \(\texttt{SATzilla}\), also gives rise to an automated method for quantifying the importance of each of a set of available solvers. We entered a substantially improved version of \(\texttt{SATzilla}\) to the inaugural “analysis track” of the 2011 SAT competition, and draw two main conclusions from the results that we obtained. First, automatically-constructed portfolios of sequential, non-portfolio competition entries perform substantially better than the winners of all three sequential categories. Second, and more importantly, a detailed analysis of these portfolios yields valuable insights into the nature of successful solver designs in the different categories. For example, we show that the solvers contributing most to \(\texttt{SATzilla}\) were often not the overall best-performing solvers, but instead solvers that exploit novel solution strategies to solve instances that would remain unsolved without them.

Authors

• Lin Xu
• Frank Hutter
• Holger H. Hoos
• Kevin Leyton-Brown

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