Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection

The AI community has achieved great success in designing high-performance algorithms for hard combinatorial problems, given both considerable domain knowledge and considerable effort by human experts. Two influential methods aim to automate this process: automated algorithm configuration and portfolio-based algorithm selection. The former has the advantage of requiring virtually no domain knowledge, but produces only a single solver; the latter exploits per-instance variation, but requires a set of relatively uncorrelated candidate solvers. Here, we introduce Hydra, a novel technique for combining these two methods, thereby realizing the benefits of both. Hydra automatically builds a set of solvers with complementary strengths by iteratively configuring new algorithms. It is primarily intended for use in problem domains for which an adequate set of candidate solvers does not already exist. Nevertheless, we tested Hydra on a widely studied domain, stochastic local search algorithms for...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Lin Xu, Holger Hoos, Kevin Leyton-Brown
• Keywords: AAAI 2010, Algorithms, Candidate Solvers, Domain Knowledge, Intelligent Agents