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Efficient solutions for the far from most string problem

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Abstract

Computational molecular biology has emerged as one of the most exciting interdisciplinary fields. It has currently benefited from concepts and theoretical results obtained by different scientific research communities, including genetics, biochemistry, and computer science. In the past few years it has been shown that a large number of molecular biology problems can be formulated as combinatorial optimization problems, including sequence alignment problems, genome rearrangement problems, string selection and comparison problems, and protein structure prediction and recognition. This paper provides a detailed description of string selection and string comparison problems. For finding good-quality solutions of a particular class of string comparison molecular biology problems, known as the far from most string problem, we propose new heuristics, including a Greedy Randomized Adaptive Search Procedure (GRASP) and a Genetic Algorithm (GA). Computational results indicate that these randomized heuristics find better quality solutions compared with results produced by the best state-of-the-art heuristic approach.

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Acknowledgements

The authors gratefully acknowledge Daniele Ferone for his help in the implementation and experimentation phase and the anonymous referees for their comments and suggestions which have been revealed useful to improve both quality and readability of the manuscript.

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Authors and Affiliations

  1. Dipartimento di Matematica e Applicazioni, Università degli Studi di Napoli Federico II, Napoli, Italy

    Paola Festa

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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  1. Paola Festa
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  2. Panos M. Pardalos
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Correspondence to Paola Festa.

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Festa, P., Pardalos, P.M. Efficient solutions for the far from most string problem. Ann Oper Res 196, 663–682 (2012). https://doi.org/10.1007/s10479-011-1028-7

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