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Expected runtimes of evolutionary algorithms for the Eulerian cycle problem

机译:欧拉循环问题的进化算法的预期运行时间

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Evolutionary algorithms are randomized search heuristics, which are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization. They have successfully been applied to different kinds of arc routing problems. To start the analysis of evolutionary algorithms with respect to the expected optimization time on these problems, we consider the Eulerian cycle problem. We show that a variant of the well-known (1 + 1) EA working on the important encoding of permutations is able to find an Eulerian tour of an Eulerian graph in expected polynomial time. Altering the operator used for mutation in the considered algorithm, the expected optimization time changes from polynomial to exponential.
机译:进化算法是随机搜索启发式算法,适用于结构不甚了解的问题以及组合优化中的问题。它们已成功地应用于各种弧线布线问题。为了针对这些问题的预期最优化时间开始分析演化算法,我们考虑了欧拉循环问题。我们表明,对排列的重要编码进行操作的著名(1 +1)EA的变体能够在期望的多项式时间内找到欧拉图的欧拉游。在考虑的算法中更改用于变异的运算符后,预期的优化时间将从多项式变为指数。

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