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On success rates for controlled random search

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Abstract

Controlled Random Search (CRS) is a simple population based algorithm which despite its attractiveness for practical use, has never been very popular among researchers on Global Optimization due to the difficulties in analysing the algorithm. In this paper, a framework to study the behaviour of algorithms in general is presented and embedded into the context of our view on questions in Global Optimization. By using as a reference a theoretical ideal algorithm called N-points Pure Adaptive Search (NPAS) some new analytical results provide bounds on speed of convergence and the Success Rate of CRS in the limit once it has settled down into simple behaviour. To relate the performance of the algorithm to characteristics of functions to be optimized, constructed simple test functions, called extreme cases, are used.

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Authors and Affiliations

  1. Group Operations Research and Logistics, Wageningen University, Hollandseweg 1, 6706 KN, Wageningen, The Netherlands

    Eligius M.T. Hendrix

  2. Computer Architecture & Electronics Department, University of Almería Cta. Sacramento SN, 04120, Almería, Spain

    P.M. Ortigosa & I. García

Authors
  1. Eligius M.T. Hendrix
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  2. P.M. Ortigosa
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  3. I. García
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Hendrix, E.M., Ortigosa, P. & García, I. On success rates for controlled random search. Journal of Global Optimization 21, 239–263 (2001). https://doi.org/10.1023/A:1012387510553

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