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Fundamental limitations on search algorithms: Evolutionary computing in perspective

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Computer Science Today

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1000))

Abstract

The past twenty years has seen a rapid growth of interest in stochastic search algorithms, particularly those inspired by natural processes in physics and biology. Impressive results have been demonstrated on complex practical optimisation problems and related search applications taken from a variety of fields, but the theoretical understanding of these algorithms remains weak. This results partly from the insufficient attention that has been paid to results showing certain fundamental limitations on universal search algorithms, including the so-called “No Free Lunch” Theorem. This paper extends these results and draws out some of their implications for the design of search algorithms, and for the construction of useful representations. The resulting insights focus attention on tailoring algorithms and representations to particular problem classes by exploiting domain knowledge. This highlights the fundamental importance of gaining a better theoretical grasp of the ways in which such knowledge may be systematically exploited as a major research agenda for the future.

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Authors and Affiliations

  1. Quadstone Ltd, 16 Chester Street, EH3 7RA, Edinburgh, UK

    Nicholas J. Radcliffe & Patrick D. Surry

  2. Department of Mathematics, University of Edinburgh, The Kings Buildings, EH9 3JZ, UK

    Nicholas J. Radcliffe & Patrick D. Surry

Authors
  1. Nicholas J. Radcliffe
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  2. Patrick D. Surry
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Jan van Leeuwen

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© 1995 Springer-Verlag Berlin Heidelberg

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Radcliffe, N.J., Surry, P.D. (1995). Fundamental limitations on search algorithms: Evolutionary computing in perspective. In: van Leeuwen, J. (eds) Computer Science Today. Lecture Notes in Computer Science, vol 1000. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015249

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  • DOI: https://doi.org/10.1007/BFb0015249

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60105-0

  • Online ISBN: 978-3-540-49435-5

  • eBook Packages: Springer Book Archive

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