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Population size and scalability in the aeseh evolutionary many-objective algorithm

Published: 06 July 2013 Publication History
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  • Abstract

    This work studies the effects of population size on performance scalability of the Adaptive ε-Sampling and ε-Hood evolutionary many-objective algorithm.

    Reference

    [1]
    H. Aguirre, A. Oyama, and K. Tanaka, "Adaptive "-Sampling and "-Hood for Evolutionary Many-Objective Optimization", EMO Conference, LNCS, Springer, pp. 322--336, vol. 7811, 2013.

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    1. Population size and scalability in the aeseh evolutionary many-objective algorithm

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      cover image ACM Conferences
      GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
      July 2013
      1798 pages
      ISBN:9781450319645
      DOI:10.1145/2464576
      • Editor:
      • Christian Blum,
      • General Chair:
      • Enrique Alba
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 06 July 2013

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      Author Tags

      1. many-objective optimization
      2. population size
      3. scalability

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      GECCO '13
      Sponsor:
      GECCO '13: Genetic and Evolutionary Computation Conference
      July 6 - 10, 2013
      Amsterdam, The Netherlands

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      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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