Population size matters: Rigorous runtime results for maximizing the hypervolume indicator
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- @Article{Nguyen:2015:TCS,
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author = "Anh Quang Nguyen and Andrew M. Sutton and
Frank Neumann",
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title = "Population size matters: Rigorous runtime results for
maximizing the hypervolume indicator",
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journal = "Theoretical Computer Science",
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volume = "561, Part A",
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pages = "24--36",
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year = "2015",
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note = "Genetic and Evolutionary Computation",
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ISSN = "0304-3975",
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DOI = "doi:10.1016/j.tcs.2014.06.023",
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URL = "http://www.sciencedirect.com/science/article/pii/S0304397514004599",
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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.",
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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
Citations