Memetic algorithms with partial lamarckism for the shortest common supersequence problem

Authors:
Carlos Cotta
Affiliations:
Dept. Lenguajes y Ciencias de la Computación, ETSI Informática, University of Málaga, Málaga, Spain
Venue:
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Year:
2005

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Abstract

The Shortest Common Supersequence problem is a hard combinatorial optimization problem with numerous practical applications. We consider the use of memetic algorithms (MAs) for solving this problem. A specialized local-improvement operator based on character removal and heuristic repairing plays a central role in the MA. The tradeoff between the improvement achieved by this operator and its computational cost is analyzed. Empirical results indicate that strategies based on partial lamarckism (i.e., moderate use of the improvement operator) are slightly superior to full-lamarckism and no-lamarckism.