Population size matters: Rigorous runtime results for maximizing the hypervolume indicator

Anh Quang Nguyen, Andrew M. Sutton, Frank Neumann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Using the hypervolume indicator to guide the search of evolutionary multi-objective algorithms has become very popular in recent years. We contribute to the theoretical understanding of these algorithms by carrying out rigorous runtime analyses. We consider multi-objective variants of the problems OneMax and LeadingOnes called OneMinMax and LOTZ, respectively, and investigate hypervolume-based algorithms with population sizes that do not allow coverage of the entire Pareto front. Our results show that LOTZ is easier to optimize than OneMinMax for hypervolume-based evolutionary multi-objective algorithms which is contrary to the results on their single-objective variants and the well-studied (1+1) EA.

Original languageEnglish (US)
Title of host publicationGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference
Pages1613-1620
Number of pages8
DOIs
StatePublished - Sep 2 2013
Event2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013 - Amsterdam, Netherlands
Duration: Jul 6 2013Jul 10 2013

Publication series

NameGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference

Other

Other2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013
CountryNetherlands
CityAmsterdam
Period7/6/137/10/13

Keywords

  • Multi-objective Optimization
  • Runtime Analysis
  • Theory

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