Abstract
Optimization algorithms for blackbox functions can be broadly split into two categories: heuristic and non-heuristic. A heuristic is any approach that, while supported by some argument of why it should succeed, does not include a guarantee of success. In the framework of blackbox optimization, we take this statement to mean that the algorithm does not employ mathematically proven stopping criterion. Hence, derivative-free optimization is distinguished from heuristic blackbox optimization, by the presence of a stopping test that provides some assurance of optimality.
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Audet, C., Hare, W. (2017). Genetic Algorithms. In: Derivative-Free and Blackbox Optimization. Springer Series in Operations Research and Financial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-68913-5_4
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DOI: https://doi.org/10.1007/978-3-319-68913-5_4
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68912-8
Online ISBN: 978-3-319-68913-5
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