Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges
Mario A Munoz, Yuan Sun, Michael Kirley, Saman K Halgamuge
Information Sciences | Elsevier | Published : 2015
Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. Algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed. In this paper, we present a survey of methods for algorithm selection in the black-..View full abstract
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Awarded by Australian Research Council
This paper is a revised, updated and expanded version of . Funding was provided by The University of Melbourne through MIRS/MIFRS scholarships awarded to Mario A. Munoz, and the Australian Research Council through Grant No. DP120103678. We thank Prof. K. Smith-Miles for her valuable comments.