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Stochastic Convergence

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Handbook of Natural Computing

Abstract

Since the state transitions of an evolutionary algorithm (EA) are of stochastic nature, the deterministic concept of the “convergence to the optimum” is not appropriate. In order to clarify the exact semantic of a phrase like “the EA converges to the global optimum” one has to, at first, establish the connection between EAs and stochastic processes before distinguishing between the various modes of stochastic convergence of stochastic processes. Subsequently, this powerful framework is applied to derive convergence results for EAs.

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Author information

Authors and Affiliations

  1. Department of Computer Science, TU Dortmund, Dortmund, Germany

    Günter Rudolph

Authors
  1. Günter Rudolph
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Editor information

Editors and Affiliations

  1. LIACS, Leiden University, Leiden, The Netherlands

    Grzegorz Rozenberg

  2. Computer Science Department, University of Colorado, Boulder, USA

    Grzegorz Rozenberg

  3. LIACS, Leiden University, Leiden, The Netherlands

    Thomas Bäck

  4. LIACS, Leiden University, Leiden, The Netherlands

    Joost N. Kok

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Rudolph, G. (2012). Stochastic Convergence. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_27

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