Abstract
The closest substring problem is a formal description of how to find a pattern such that from a given set of strings a subregion of each string is highly similar to that pattern. This problem appears frequently in computational biology and in coding theory. Experimental results suggest that this NP-hard optimization problem can be approached very well with a custom-built evolutionary algorithm using a fixed-length string representation, as in the typical genetic algorithm (GA) concept. Part of this success can be attributed to a novel mutation operator introduced in this paper. For practical purposes, the GA used here seems to be an improvement compared to traditional approximation algorithms. While the time complexity of traditional approximation algorithms can be analyzed precisely, they suffer from poor run-time efficiency or poor accuracy, or both.
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Mauch, H. (2004). Closest Substring Problem – Results from an Evolutionary Algorithm. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_30
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DOI: https://doi.org/10.1007/978-3-540-30499-9_30
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