Icarus: Identification Of Complementary Algorithms By Uncovered Sets

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 using a suite of state-of-the-art evolutionary algorithms and benchmark continuous optimization problems. Our analysis confirms that ICARUS creates an algorithm portfolio where the expected performance is superior to a manually constructed portfolio.