A comparison of evolutionary approaches to the shortest common supersequence problem

Authors:
Carlos Cotta
Affiliations:
Dept. Lenguajes y Ciencias de la Computación, ETSI Informática, University of Málaga, Málaga, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

The Shortest Common Supersequence problem is a hard combinatorial optimization problem with numerous practical applications. Several evolutionary approaches are proposed for this problem, considering the utilization of penalty functions, GRASP-based decoders, or repairing mechanisms. An empirical comparison is conducted, using an extensive benchmark comprising problem instances of different size and structure. The empirical results indicate that there is no single best approach, and that the size of the alphabet, and the structure of strings are crucial factors for determining performance. Nevertheless, the repair-based EA seems to provide the best performance tradeoff.