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Computing Minimum Cuts by 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 bi-criteria approach based on the famous maximum-flow minimum-cut theorem that enables evolutionary algorithms to find an optimal solution in expected polynomial time.


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Correspondence to Frank Neumann.

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A conference version appeared in GECCO 2008 [17].

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Neumann, F., Reichel, J. & Skutella, M. Computing Minimum Cuts by Randomized Search Heuristics. Algorithmica 59, 323–342 (2011).

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  • Evolutionary algorithms
  • Minimum s-t-cuts
  • Multi-objective optimization
  • Randomized search heuristics