On the Runtime Analysis of Fitness Sharing Mechanisms

  • Pietro S. Oliveto
  • Dirk Sudholt
  • Christine Zarges
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8672)

Abstract

Fitness sharing is a popular diversity mechanism implementing the idea that similar individuals in the population have to share resources and thus, share their fitnesses. Previous runtime analyses of fitness sharing studied a variant where selection was based on populations instead of individuals. We use runtime analysis to highlight the benefits and dangers of the original fitness sharing mechanism on the well-known test problem TwoMax, where diversity is crucial for finding both optima. In contrast to population-based sharing, a (2+1) EA in the original setting does not guarantee finding both optima in polynomial time; however, a (μ+1) EA with μ ≥ 3 always succeeds in expected polynomial time. We further show theoretically and empirically that large offspring populations in (μ + λ) EAs can be detrimental as overpopulation can make clusters of search points go extinct.

Keywords

Evolutionary computation diversity mechanisms fitness sharing runtime analysis 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pietro S. Oliveto
    • 1
  • Dirk Sudholt
    • 1
  • Christine Zarges
    • 2
  1. 1.University of SheffieldSheffieldUK
  2. 2.University of BirminghamBirminghamUK

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