Many real-world optimization problems involve uncertainty. In this paper, we consider the case of worst-case optimization, i.e., the user is interested in a solution's performance in the worst case only. If the number of possible scenarios is large, it is an optimization problem by itself to determine a solution's worst case performance. In this paper, we apply coevolutionary algorithms to co-evolve the worst case test cases along with the solution candidates. We propose a number of new variants of coevolutionary algorithms, and show that these techniques outperform previously proposed coevolutionary worst-case optimizers on some simple test problems. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Branke, J., & Rosenbusch, J. (2008). New approaches to coevolutionary worst-case optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5199 LNCS, pp. 144–153). https://doi.org/10.1007/978-3-540-87700-4_15
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