Improved heuristics and a genetic algorithm for finding short supersequences

被引:24
作者
Branke J. [1 ]
Middendorf M. [1 ]
Schneider F. [1 ]
机构
[1] Institut für Angewandte Informatik und Formale Beschreibungsverfahren, Universität Karlsruhe
关键词
Genetic algorithms; Heuristics; Supersequences;
D O I
10.1007/BF01545528
中图分类号
学科分类号
摘要
In this paper several heuristics and a genetic algorithm (GA) are described for the Shortest Common Supersequence (SCS) problem, an NP-complete problem with applications in production planning, mechanical engineering and data compression. While our heuristics show the same worst case behaviour as the classical Majority Merge heuristic (MM) they outperform MM on nearly all our test instances. We furthermore present a genetic algorithm based on a slightly modified version of one of the new heuristics. The resulting GA/heuristic hybrid yields significantly better results than any of the heuristics alone, though the running time is much higher. © Springer-Verlag 1998.
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页码:39 / 45
页数:6
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