Latent Class Models for Algorithm Portfolio Methods

Different solvers for computationally difficult problems such as satisfiability (SAT) perform best on different instances. Algorithm portfolios exploit this phenomenon by predicting solvers' performance on specific problem instances, then shifting computational resources to the solvers that appear best suited. This paper develops a new approach to the problem of making such performance predictions: natural generative models of solver behavior. Two are proposed, both following from an assumption that problem instances cluster into latent classes: a mixture of multinomial distributions, and a mixture of Dirichlet compound multinomial distributions. The latter model extends the former to capture burstiness, the tendency of solver outcomes to recur. These models are integrated into an algorithm portfolio architecture and used to run standard SAT solvers on competition benchmarks. This approach is found competitive with the most prominent existing portfolio, SATzilla, which relies on ...

• Type: Journal
• Year: 2010
• Where: AAAI
• Authors: Bryan Silverthorn, Risto Miikkulainen
• Keywords: AAAI 2010, Algorithm Portfolio, Intelligent Agents, Multinomial Distributions, Specific Problem Instances