A data-based coding of candidate strings in the closest string problem

Authors:
Bryant A. Julstrom
Affiliations:
St. Cloud State University, St. Cloud, MN, USA
Venue:
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Year:
2009

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Abstract

Given a set of strings S of equal lengths over an alphabet σ, the closest string problem seeks a string over σ whose maximum Hamming distance to any of the given strings is as small as possible. A data-based coding of strings for evolutionary search represents candidate closest strings as sequences of indexes of the given strings. The string such a chromosome represents consists of the symbols in the corresponding positions of the indexed strings. A genetic algorithm using this coding was compared with two GAs that encoded candidate strings directly as strings over σ. In trials on twenty-five instances of the closest string problem with alphabets ranging is size from 2 to 30, the algorithm that used the data-based representation of candidate strings consistently returned the best results, and its advantage increased with the sizes of the test instances' alphabets.