Given a set S={s1,s2,…,sn}\documentclass[12pt]{minimal}
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\begin{document}$$S = \{s_1, s_2, \ldots , s_n\}$$\end{document} of strings of equal length L\documentclass[12pt]{minimal}
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\begin{document}$$L$$\end{document} and an integer d\documentclass[12pt]{minimal}
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\begin{document}$$d$$\end{document}, the closest string problem (CSP) requires the computation of a string s\documentclass[12pt]{minimal}
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\begin{document}$$s$$\end{document} of length L\documentclass[12pt]{minimal}
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\begin{document}$$L$$\end{document} such that d(s,si)≤d\documentclass[12pt]{minimal}
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\begin{document}$$d(s, s_i) \le d$$\end{document} for each si∈S\documentclass[12pt]{minimal}
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\begin{document}$$s_i \in S$$\end{document}, where d(s,si)\documentclass[12pt]{minimal}
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\begin{document}$$d(s, s_i)$$\end{document} is the Hamming distance between s\documentclass[12pt]{minimal}
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\begin{document}$$s$$\end{document} and si\documentclass[12pt]{minimal}
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\begin{document}$$s_i$$\end{document}. The problem is NP-hard and has been extensively studied in the context of approximation algorithms and fixed-parameter algorithms. Fixed-parameter algorithms provide the most practical solutions to its real-life applications in bioinformatics. In this paper we develop the first randomized fixed-parameter algorithms for CSP. Not only are the randomized algorithms much simpler than their deterministic counterparts, their time complexities are also significantly better than the previously best known (deterministic) algorithms.
