Minimum spanning trees made easier via multi-objective optimization

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Minimum spanning trees made easier via multi-objective optimization
Many real-world problems are multi-objective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front, multi-objective optimization problems are considered as more difficult than single-objective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore, it is possible that a singleobjective problem can be solved more efficiently via a generalized multi-objective model of the problem. That this is indeed the case is proved by investigating the computation of minimum spanning trees. Categories and Subject Descriptors F.2 [Theory of Computation]: Analysis of Algorithms and Problem Complexity General Terms Algorithms, Experimentation, Performance, Theory Keywords Multi-objective optimization, Working principles of evolutionary computing, Running time analy...
Frank Neumann, Ingo Wegener
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Authors Frank Neumann, Ingo Wegener
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