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Preprint 03-2008

Combinatorial Optimization & Graph Algorithms group (COGA-Preprints)

Computing Minimum Cuts by Randomized Search Heuristics
An extended abstract appeared in Proceedings of the 10th Genetic and Evolutionary Computation Conference (GECCO'08), 779-787, 2008.
not available
evolutionary algorithms, minimum s-t-cuts, multi-objective optimization, randomized search heuristics
We study the minimum s-t-cut problem in graphs with costs on the edges in the context of evolutionary algorithms. Minimum cut problems belong to the class of basic network optimization problems that occur as crucial subproblems in many real-world optimization problems and have a variety of applications in several different areas. We prove that there exist instances of the minimum s-t-cut problem that cannot be solved by standard single-objective evolutionary algorithms in reasonable time. On the other hand, we develop a bicriteria approach based on the famous MaxFlow-MinCut Theorem that enables evolutionary algorithms to find an optimum solution in expected polynomial time.
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