Abstract
Robust analysis is important for designing and analyzing algorithm for global optimization. In this paper, we introduced a new concept, robust constant, to quantitatively characterize robustness of measurable sets and measurable functions. The new concept is consistent with the robustness proposed in literature. This paper also showed that robust constant had significant value in the analysis of some random search algorithms for solving global optimization problem.
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Acknowledgements
This work is supported by Natural Science Foundation of China (61170308), Natural Science Foundation of FuJian Province (2011J01008) and the talent foundation of Fuzhou University (XRC-1043).
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Peng, Z., Wu, D., Zhu, W. (2015). The Robust Constant and Its Applications in Global Optimization. In: Gao, D., Ruan, N., Xing, W. (eds) Advances in Global Optimization. Springer Proceedings in Mathematics & Statistics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-08377-3_45
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DOI: https://doi.org/10.1007/978-3-319-08377-3_45
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