SATzilla: Portfolio-based Algorithm Selection for SAT

It has been widely observed that there is no single "dominant" SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use socalled empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 b...

• Type: Journal
• Year: 2008
• Where: JAIR
• Authors: Lin Xu, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown
• Keywords: Constituent Solvers, Hardness Models, JAIR 2008, Problem Instances