Abstract
The genomic median problem is an optimization problem inspired by a biological issue: it aims to find the chromosome organization of the common ancestor to multiple living species. It is formulated as the search for a genome that minimizes a rearrangement distance measure among given genomes. Several attempts have been reported for solving this NP-hard problem. These range from simple heuristic methods to a stochastic local search algorithm inspired by WalkSAT, a well-known local search algorithm for the satisfiability problem in propositional logic.
The main objective of this research is to develop improved algorithmic techniques for tackling the genomic median problem and to provide new state-of-the-art solutions. In particular, we have developed an algorithm that is based on tabu search and iterated local search and that shows high performance. To alleviate the dependence of the algorithm performance on a single fixed parameter setting, we have included a reactive scheme that automatically adapts the tabu list length of the tabu search part and the perturbation strength of the iterated local search part. In fact, computational results show that we have developed a new very high-performing stochastic local search algorithm for the genomic median problem and we also have found a new best solution for a real-world case.
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References
Moret, B., Tang, J., Warnow, T.: Reconstructing phylogenies from gene content and gene-order data. In: Gascuel, O. (ed.) Mathematics of Evolution and Phylogeny, pp. 321–352. Oxford Univ. Press, Oxford
Bernt, M., Merkle, D., Middendorf, M.: Using median sets for inferring phylogenetic trees. Bioinformatics 23, e129–e135 (2007)
Hannenhalli, S., Pevzner, P.A.: Transforming cabbage into turnip: Polynomial algorithm for sorting signed permutations by reversals. Journal of the ACM 46(1), 1–27 (1999)
Yancopoulos, S., Attie, O., Friedberg, R.: Efficient sorting of genomic permutations by translocation, inversion and block interchange. bioinformatics 21(16), 3340–3346 (2005)
Caprara, A.: The reversal median problem. INFORMS Journal on Computing 15, 93–113 (2003)
Moret, B., Siepel, A., Tang, J., Liu, T.: Inversion medians outperform breakpoint medians in phylogeny reconstruction from gene-order data (2002)
Arndt, W., Tang, J.: Improving inversion median computation using commuting reversals and cycle information. In: Tesler, G., Durand, D. (eds.) RECMOB-CG 2007. LNCS (LNBI), vol. 4751, pp. 30–44. Springer, Heidelberg (2007)
Bourque, G., Pevzner, P.: Genome-scale evolution: Reconstructing gene orders in the ancestral species. Genome Res. 12(1), 26–36 (2002)
Interian, Y., Durrett, R.: Genomic midpoints: Computation and evolutionary implications (Submitted, 2007)
Glover, F., Laguna, F.: Tabu Search. Kluwer Academic Publishers, Norwell, MA, USA (1997)
Stutzle, T.: Iterated local search for the quadratic assignment problem (1999)
Bergeron, A., Mixtacki, J., Stoye, J.: A unifying view of genome rearrangements. In: Bücher, P., Moret, B.M.E. (eds.) WABI 2006. LNCS (LNBI), vol. 4175, pp. 163–173. Springer, Heidelberg (2006)
Alekseyev, M.A., Pevzner, P.A.: Multi-break rearrangements and chromosomal evolution. Theoretical Computer Science (to appear, 2007)
Selman, B., Kautz, H., Cohen, B.: Noise strategies for improving local search. In: Proceedings of the 12th National Conference on Artificial Intelligence, pp. 337–343. AAAI Press / The MIT Press, Menlo Park, CA, USA (1994)
van Hemert, J., Bäck, T.: Measuring the searched space to guide efficiency: The principle and evidence on constraint satisfaction. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 23–32. Springer, Heidelberg (2002)
Birattari, M.: race: Racing methods for the selection of the best. R package version 0.1.56 (2005)
Birattari, M.: The Problem of Tuning Metaheuristics as Seen from a Machine Learning Perspective. PhD thesis, Université Libre de Bruxelles, Brussels, Belgium (2004)
Battiti, R., Protasi, M.: Reactive local search for the maximum clique problem. Algorithmica 29(4), 610–637 (2001)
Eriksen, N.: Reversal and transposition medians. Theoretical Computer Science 374(1-3) (2007)
Lenne, R., Solnon, C., Sttzle, T., Tannier, E., Birattari, M.: Effective Stochastic Local Search Algorithms for the Genomic Median Problem. In: Doctoral Symposium on Engineering Stochastic Local Search Algorithms (SLS-DS). IRIDIA Technical Report Series, pp. 1–5 (2007)
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Lenne, R., Solnon, C., Stützle, T., Tannier, E., Birattari, M. (2008). Reactive Stochastic Local Search Algorithms for the Genomic Median Problem. In: van Hemert, J., Cotta, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2008. Lecture Notes in Computer Science, vol 4972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78604-7_23
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DOI: https://doi.org/10.1007/978-3-540-78604-7_23
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