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On a new stochastic global optimization algorithm based on censored observations

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Abstract

In this paper a new algorithm is proposed for global optimization problems. The main idea is that of modifying a standard clustering approach by sequentially sampling the objective function while adaptively deciding an appropriate sample size. Theoretical as well as computational results are presented.

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Authors and Affiliations

  1. Department of Computer Sciences, University of Milano, via Comelico 39/41, I-20135, Milano, Italy

    Fabio Schoen

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  1. Fabio Schoen
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Schoen, F. On a new stochastic global optimization algorithm based on censored observations. J Glob Optim 4, 17–35 (1994). https://doi.org/10.1007/BF01096532

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  • DOI: https://doi.org/10.1007/BF01096532

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