Abstract
In black-box optimization a search algorithm looks for a global optimum of an unknown objective function that can only be explored by sampling the function values of some points in the search space. Black-box complexity measures the number of such function evaluations that any search algorithms needs to make in the worst case to locate a global optimum of any objective function from some class of functions. The black-box complexity of a function class thus yields a lower bound on the performance for all algorithms. This chapter gives a precise and accessible introduction to the notion of black-box complexity, explains important properties and discusses several concrete examples. Starting with simple examples and proceeding step-wise to more complex examples an introduction that explains how such results can be derived is presented.
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This material is based upon work supported by Science Foundation Ireland (SFI) under Grant No. 07/SK/I1205.
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Jansen, T. (2014). Black-Box Complexity for Bounding the Performance of Randomized Search Heuristics. In: Borenstein, Y., Moraglio, A. (eds) Theory and Principled Methods for the Design of Metaheuristics. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33206-7_5
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DOI: https://doi.org/10.1007/978-3-642-33206-7_5
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