ICARUS: Identification of Complementary algoRithms by Uncovered Sets

Abstract

Since there is no single best performing algorithm for all problems, an algorithm portfolio would leverage the strengths of complementary algorithms to achieve the best performance. In this paper, we present and evaluate a new technique for designing algorithm portfolios for continuous black-box optimization problems, based on social choice and voting theory concepts. Our technique, which we call ICARUS, models the portfolio design task as an election, in which each problem 'votes' for a subset of preferred algorithms guided by a performance metric such as the number of fitness evaluations. The resulting 'uncovered set' of algorithms forms the portfolio. We demonstrate the efficacy of ICARUS..