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A greedy randomized adaptive search procedure with path relinking for the shortest superstring problem

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Abstract

The shortest superstring problem (SSP) is an \(NP\)-hard combinatorial optimization problem which has attracted the interest of many researchers, due to its applications in computational molecular biology problems such as DNA sequencing, and in computer science problems such as string compression. In this paper a new heuristic algorithm for solving large scale instances of the SSP is presented, which outperforms the natural greedy algorithm in the majority of the tested instances. The proposed method is able to provide multiple near-optimum solutions and admits a natural parallel implementation. Extended computational experiments on a set of SSP instances with known optimum solutions indicate that the new method finds the optimum solution in most of the cases, and its average error relative to the optimum is close to zero.

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Acknowledgments

This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: Heraclitus II. Investing in knowledge society through the European Social Fund.

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  1. Department of Mathematical, Physical and Computational Sciences, Aristotle University of Thessaloniki, 54124 , Thessaloniki, Greece

    Theodoros Gevezes & Leonidas Pitsoulis

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  1. Theodoros Gevezes
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  2. Leonidas Pitsoulis
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Correspondence to Theodoros Gevezes.

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Gevezes, T., Pitsoulis, L. A greedy randomized adaptive search procedure with path relinking for the shortest superstring problem. J Comb Optim 29, 859–883 (2015). https://doi.org/10.1007/s10878-013-9622-z

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