A new algorithm for reducing metaheuristic design effort

Maria Cristina Riff, Elizabeth Montero

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

29 Citations (Scopus)

Abstract

The process of designing a metaheuristic is a difficult and time consuming task as it usually requires tuning to find the best associated parameter values. In this paper, we propose a simple tuning tool called EVOCA which allows unexperimented metaheuristic designers to obtain good quality results without have a strong knowledge in tuning methods. The simplicity here means that the designer does not have to care about the initial settings of the tuner. We apply EVOCA to a genetic algorithm that solves NK landscape instances of various categories. We show that EVOCA is able to tune both categorical and numerical parameters allowing the designer to discard ineffective components for the algorithm.

Original languageEnglish
Title of host publication2013 IEEE Congress on Evolutionary Computation, CEC 2013
Pages3283-3290
Number of pages8
DOIs
Publication statusPublished - 21 Aug 2013
Event2013 IEEE Congress on Evolutionary Computation, CEC 2013 - Cancun, Mexico
Duration: 20 Jun 201323 Jun 2013

Conference

Conference2013 IEEE Congress on Evolutionary Computation, CEC 2013
Country/TerritoryMexico
CityCancun
Period20/06/1323/06/13

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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