New Approaches to Coevolutionary Worst-Case Optimization
- Cite this paper as:
- Branke J., Rosenbusch J. (2008) New Approaches to Coevolutionary Worst-Case Optimization. In: Rudolph G., Jansen T., Beume N., Lucas S., Poloni C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg
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.
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