Memetic Algorithms with Partial Lamarckism for the Shortest Common Supersequence Problem

摘要

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.