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

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@Article{Nguyen:2015:TCS,
  author =       "Anh Quang Nguyen and Andrew M. Sutton and 
                 Frank Neumann",
  title =        "Population size matters: Rigorous runtime results for
                 maximizing the hypervolume indicator",
  journal =      "Theoretical Computer Science",
  volume =       "561, Part A",
  pages =        "24--36",
  year =         "2015",
  note =         "Genetic and Evolutionary Computation",
  ISSN =         "0304-3975",
  DOI =          "doi:10.1016/j.tcs.2014.06.023",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0304397514004599",
  abstract =     "Evolutionary multi-objective optimisation is one of
                 the most successful areas in the field of evolutionary
                 computation. 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 optimise 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.
                 Furthermore, we study multi-objective genetic
                 programming using the hypervolume indicator. We show
                 that the classical ORDER problem is easy to optimise if
                 the population size is large enough to cover the whole
                 Pareto front and point out situations where a small
                 population size leads to an exponential optimisation
                 time.",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 multi-objective optimisation, Hypervolume indicator,
                 Theory, Runtime time analysis",
}

Genetic Programming entries for Anh Quang Nguyen Andrew M Sutton Frank Neumann

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