随机植被顶点覆盖上 (1+1) 进化算法的固定参数可操作性

Jack Kearney, Frank Neumann, Andrew M. Sutton
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引用次数: 0

摘要

我们首次对顶点覆盖分布问题上的标准(1+1)进化算法进行了参数化分析。我们证明,如果种植覆盖最多为对数,那么对于足够密集的随机图 $p > 0.71$,每隔 $O(n \logn)$ 步重新启动 (1+1) 进化算法将在多项式时间内找到一个至少与种植覆盖一样小的覆盖。对于超对数植被覆盖,我们证明了 (1+1) EA 在期望时间内找到了一个无固定参数的解决方案。我们用大量计算实验来补充这些理论研究,这些实验突出了植被大小、图密度和运行时间之间的相互作用。
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Fixed-Parameter Tractability of the (1+1) Evolutionary Algorithm on Random Planted Vertex Covers
We present the first parameterized analysis of a standard (1+1) Evolutionary Algorithm on a distribution of vertex cover problems. We show that if the planted cover is at most logarithmic, restarting the (1+1) EA every $O(n \log n)$ steps will find a cover at least as small as the planted cover in polynomial time for sufficiently dense random graphs $p > 0.71$. For superlogarithmic planted covers, we prove that the (1+1) EA finds a solution in fixed-parameter tractable time in expectation. We complement these theoretical investigations with a number of computational experiments that highlight the interplay between planted cover size, graph density and runtime.
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