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An exploration into dynamic population sizing

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Published:07 July 2010Publication History

ABSTRACT

Traditional evolutionary algorithms are powerful problem solvers that have several fixed parameters which require prior specification. Determining good values for any of these parameters can be difficult, as these parameters are generally very sensitive, requiring expert knowledge to set optimally without extensive use of trial and error. Parameter control is a promising approach to achieving this automation and has the added potential of increasing EA performance based on both theoretical and empirical evidence that the optimal values of EA strategy parameters change during the course of executing an evolutionary run. While many methods of parameter control have been published that focus on removing the population size parameter, μ, all hampered by a variety of problems. This paper investigates the benefits of making μ a dynamic parameter and introduces two novel methods for population control. These methods are then compared to state-of-the-art population sizing EAs, exploring the strengths and weaknesses of each.

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              cover image ACM Conferences
              GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
              July 2010
              1520 pages
              ISBN:9781450300728
              DOI:10.1145/1830483

              Copyright © 2010 ACM

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              Publication History

              • Published: 7 July 2010

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