ICARUS: identification of complementary algorithms by uncovered sets

Mario A. Munoz, Michael Kirley

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2016 IEEE Congress on Evolutionary Computation (CEC)
EditorsCarlos A. Coello Coello, Garrison W. Greenwood, Sanaz Mostaghim, Yuhui Shi, Chuan-Kang Ting
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781509006229
ISBN (Print)9781509006229
Publication statusPublished - 14 Nov 2016
EventIEEE Congress on Evolutionary Computation 2016 - Vancouver Convention Centre, Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016
https://ieeexplore.ieee.org/xpl/conhome/7636124/proceeding (Proceedings)


ConferenceIEEE Congress on Evolutionary Computation 2016
Abbreviated titleIEEE CEC 2016
Internet address


  • Algorithm portfolios
  • Black-box optimization
  • Continuous optimization
  • Heuristics

Cite this