Bibliography

  1. [ACH 09] ACHTERBERG T., “SCIP: solving constraint integer programs”, Mathematical Programming Computation, vol. 1, no. 1, pp. 1–41, 2009.
  2. [ADA 00] ADAMS M.D., CELNIKER S.E., HOLT R.A. et al., “The genome sequence of Drosophila melanogaster”, Science, vol. 287, no. 5461, pp. 2185–2195, 2000.
  3. [ADI 08] ADI S.S., BRAGA M.D.V., FERNANDES C.G. et al., “Repetition-free longest common subsequence”, Electronic Notes in Discrete Mathematics, vol. 30, pp. 243–248, 2008.
  4. [ADI 10] ADI S.S., BRAGA M.D.V., FERNANDES C.G. et al., “Repetition-free longest common subsquence”, Discrete Applied Mathematics, vol. 158, pp. 1315–1324, 2010.
  5. [AHO 83] AHO A., HOPCROFT J., ULLMAN J., Data Structures and Algorithms, Addison-Wesley, Reading, MA, 1983.
  6. [AIE 02] AIEX R.M., RESENDE M.G.C., RIBEIRO C.C., “Probability distribution of solution time in grasp: an experimental investigation”, Journal of Heuristics, vol. 8, pp. 343–373, 2002.
  7. [AKE 09] AKEB H., HIFI M., MHALLAH R., “A beam search algorithm for the circular packing problem”, Computers & Operations Research, vol. 36, no. 5, pp. 1513–1528, 2009.
  8. [BÄC 97] BÄCK T., FOGEL D.B., MICHALEWICZ Z., Handbook of Evolutionary Computation, Oxford University Press, 1997.
  9. [BAI 88] BAINS W., SMITH G.C., “A novel method for nucleid acid sequence determination”, Journal of Theoretical Biology, vol. 135, pp. 303–307, 1988.
  10. [BEN 10] BENEDETTINI S., BLUM C., ROLI A., “A randomized iterated greedy algorithm for the founder sequence reconstruction problem”, in BLUM C., BATTITI R. (eds), Proceedings of LION 4 – Fourth Learning and Intelligent Optimization Conference, Lecture Notes in Computer Science, vol. 6073, Springer-Verlag, Berlin, 2010.
  11. [BEN 15] BEN ALI A., LUQUE G., ALBA E. et al., “An improved problem aware local search algorithm for the DNA fragment assembly problem”, Soft Computing, 2015.
  12. [BER 98] BERGROTH L., HAKONEN H., RAITA T., “New approximation algorithms for longest common subsequences”, Proceedings of String Processing and Information Retrieval: A South American Symposium, pp. 32–40, 1998.
  13. [BER 00] BERGROTH L., HAKONEN H., RAITA T., “A survey of longest common subsequence algorithms”, Proceedings of SPIRE 2000 – 7th International Symposium on String Processing and Information Retrieval, pp. 39–48, 2000.
  14. [BŁA 99] BŁAŻEWICZ J., FORMANOWICZ P., KASPRZAK M. et al., “DNA sequencing with positive and negative errors”, Journal of Computational Biology, vol. 6, pp. 113–123, 1999.
  15. [BŁA 00] BŁAŻEWICZ J., FORMANOWICZ P., KASPRZAK M. et al., “Tabu search for DNA sequencing with false negatives and false positives”, European Journal of Operational Research, vol. 125, pp. 257–265, 2000.
  16. [BŁA 02a] BŁAŻEWICZ J., FORMANOWICZ P., GUINAND F. et al., “A heuristic managing errors for DNA sequencing”, Bioinformatics, vol. 18, no. 5, pp. 652–660, 2002.
  17. [BŁA 02b] BŁAŻEWICZ J., KASPRZAK M., KUROCZYCKI W., “Hybrid genetic algorithm for DNA sequencing with errors”, Journal of Heuristics, vol. 8, pp. 495–502, 2002.
  18. [BŁA 04] BŁAŻEWICZ J., GLOVER F., KASPRZAK M., “DNA sequencing—Tabu and scatter search combined”, INFORMS Journal on Computing, vol. 16, no. 3, pp. 232–240, 2004.
  19. [BŁA 05] BŁAŻEWICZ J., GLOVER F., KASPRZAK M., “Evolutionary approaches to DNA sequencing with errors”, Annals of Operations Research, vol. 138, pp. 67–78, 2005.
  20. [BŁA 11] BŁAŻEWICZ J., BURKE E.K., KENDALL G. et al., “A hyper-heuristic approach to sequencing by hybridization of DNA sequences”, Annals of Operations Research, vol. 207, no. 1, pp. 27–41, 2011.
  21. [BLU 03] BLUM C., ROLI A., “Metaheuristics in combinatorial optimization: overview and conceptual comparison”, ACM Computing Surveys, vol. 35, no. 3, pp. 268–308, 2003.
  22. [BLU 04] BLUM C., DORIGO M., “The hyper-cube framework for ant colony optimization”, IEEE Transactions on Man, Systems and Cybernetics – Part B, vol. 34, no. 2, pp. 1161–1172, 2004.
  23. [BLU 05] BLUM C., “Beam-ACO—Hybridizing ant colony optimization with beam search: an application to open shop scheduling”, Computers & Operations Research, vol. 32, no. 6, pp. 1565–1591, 2005.
  24. [BLU 08] BLUM C., YÁBAR VALLÈS M., BLESA M.J., “An ant colony optimization algorithm for DNA sequencing by hybridization”, Computers & Operations Research, vol. 35, no. 11, pp. 3620–3635, 2008.
  25. [BLU 09] BLUM C., BLESA M.J., LÓPEZ-IBÁNEZ M., “Beam search for the longest common subsequence problem”, Computers & Operations Research, vol. 36, no. 12, pp. 3178–3186, 2009.
  26. [BLU 10] BLUM C., “Beam-ACO for the longest common subsequence problem”, Proceedings of CEC 2010 – Congress on Evolutionary Computation, vol. 2, Piscataway, NJ, pp. 1–8, 2010.
  27. [BLU 13] BLUM C., “Construct, merge, solve & adapt: application to unbalanced minimum common string partition”, in BLESA M.J., BLUM C., CANGELOSI A. et al. (eds), Proceedings of HM 2016 Eighth International Workshop on Hybrid Metaheuristics, vol. 9668, Springer-Verlag, 2013.
  28. [BLU 14a] BLUM C., LOZANO J.A., PINACHO DAVIDSON P., “Iterative probabilistic tree search for the minimum common string partition problem”, in BLESA M.J., BLUM C., VOSS S. (eds), Proceedings of HM 2010 4–9th International Workshop on Hybrid Metaheuristics, vol. 8457, Springer, 2014.
  29. [BLU 14b] BLUM C., BLESA M.J., CALVO B., “Beam-ACO for the repetition-free longest common subsequence problem”, in LEGRAND P., CORSINI M.-M., HAO J.-K. et al. (eds), Proceedings of EA 2013 – 11th Conference on Artificial Evolution, vol. 8752, Springer-Verlag, Berlin, 2014.
  30. [BLU 14c] BLUM C., FESTA P., “A hybrid ant colony optimization algorithm for the far from most string problem”, Proceedings of EvoCOP 2014 – 14th European Conference on Evolutionary Computation in Combinatorial Optimisation, vol. 8600, Springer-Verlag, 2014.
  31. [BLU 15a] BLUM C., CALVO B., “A matheuristic for the minimum weight rooted arborescence problem”, Journal of Heuristics, vol. 21, no. 4, pp. 479–499, 2015.
  32. [BLU 15b] BLUM C., LOZANO J.A., PINACHO DAVIDSON P., “Mathematical programming strategies for solving the minimum common string partition problem”, European Journal of Operational Research, vol. 242, no. 3, pp. 769–777, 2015.
  33. [BLU 16a] BLUM C., BLESA M.J., “Construct, merge, solve & adapt: application to the repetition-free longest common subsequence problem”, in CHICANO F., HU B. (eds), Proceedings of EvoCOP 2016 – 16th European Conference on Evolutionary Computation in Combinatorial Optimization, vol. 9595, Springer-Verlag, Berlin, 2016.
  34. [BLU 16b] BLUM C., PINACHO P., LÓPEZ-IBÁNEZ M., LOZANO J.A., “Construct, merge, solve & adapt: a new general algorithm for combinatorial optimization”, Computers & Operations Research, vol. 68, pp. 75–88, 2016.
  35. [BLU 16c] BLUM C., FESTA P., “Selected string problems”, in RESENDE M.G.C., MARTÌ R., PARDALOS P.M. (eds), Handbook of Heuristics, Springer-Verlag, 2016.
  36. [BLU 16d] BLUM C., RAIDL G.R., “Computational performance evaluation of two integer linear programming models for the minimum common string partition problem”, Optimization Letters, vol. 10, no. 1, pp. 189–205, 2016.
  37. [BLU 16e] BLUM C., RAIDL G.R., Hybrid Metaheuristics – Powerful Tools for Optimization, Springer, 2016.
  38. [BON 01] BONIZZONI P., DELLA VEDOVA G., MAURI G., “Experimenting an approximation algorithm for the LCS”, Discrete Applied Mathematics, vol. 110, no. 1, pp. 13–24, 2001.
  39. [BON 07] BONIZZONI P., DELLA VEDOVA G., DONDI R. et al., “Exemplar longest common subsequence”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 535–543, 2007.
  40. [BON 10] BONIZZONI P., DELLA VEDOVA G., DONDI R. et al., “Variants of constrained longest common subsequence”, Information Processing Letters, vol. 110, no. 20, pp. 877–881, 2010.
  41. [BOU 13] BOUCHER C., LANDAU G.M., LEVY A. et al., “On approximating string selection problems with outliers”, Theoretical Computer Science, vol. 498, pp. 107–114, 2013.
  42. [BRE 00] BRENNER S., JOHNSON M., BRIDGHAM J. et al., “Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays”, Nature Biotechnology, vol. 18, no. 6, pp. 630–634, 2000.
  43. [BRI 04a] BRIZUELA C.A., GONZ ÁLEZ L.C., ROMERO H.J., “An improved genetic algorithm for the sequencing by hybridization problem”, in RAIDL G.R., CAGNONI S. et al. (eds), Proceedings of the EvoWorkshops – Applications of Evolutionary Computing: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART and EvoSTOC, vol. 3005, Springer-Verlag, Berlin, 2004.
  44. [BRI 04b] BRISK P., KAPLAN A., SARRAFZADEH M., “Area-efficient instruction set synthesis for reconfigurable system-on-chip design”, Proceedings of the 41st Design Automation Conference, pp. 395–400, 2004.
  45. [BRU 03] BRUDNO M., CHAPMAN M., GÖTTGENS B. et al., “Fast and sensitive multiple alignment of large genomic sequences”, BMC Bioinformatics, vol. 4, no. 66, pp. 1–11, 2003.
  46. [BUI 04] BUI T.N., YOUSSEF W.A., “An enhanced genetic algorithm for DNA sequencing by hybridization with positive and negative errors”, in DEB K., POLI R. et al. (eds), Proceedings of GECCO 2004 – Genetic and Evolutionary Computation Conference, vol. 3103, Springer-Verlag, Berlin, 2004.
  47. [BUL 13] BULTEAU L., FERTIN G., KOMUSIEWICZ C. et al., “A fixed-parameter algorithm for minimum common string partition with few duplications”, in DARLING A., STOYE J. (eds), Proceedings of WABI 2013 – Algorithms in Bioinformatics, vol. 8126, Springer, Berlin, 2013.
  48. [CAN 01] CANUTO S.A., RESENDE M.G.C., RIBEIRO C.C., “Local search with perturbations for the prize-collecting Steiner tree problem in graphs”, Networks, vol. 38, pp. 50–58, 2001.
  49. [CAS 13] CASTELLI M., BERETTA S., VANNESCHI L., “A hybrid genetic algorithm for the repetition free longest common subsequence problem”, Operations Research Letters, vol. 41, no. 6, pp. 644–649, 2013.
  50. [CER 85] CERNY V., “A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm”, Journal of Optimization Theory and Applications, vol. 45, pp. 41–51, 1985.
  51. [CHE 05] CHEN X., ZHENG J., FU Z. et al., “Computing the assignment of orthologous genes via genome rearrangement”, Proceedings of the Asia Pacific Bioinformatics Conference, pp. 363–378, 2005.
  52. [CHE 06] CHEN Y., PAN Y., CHEN J. et al., “Multiple sequence alignment by ant colony optimization and divide-and-conquer”, Proceedings of ICCS 2006 – 6th International Conference on Computational Science, pp. 646–653, 2006.
  53. [CHE 11a] CHEN Y., HU J., “Accurate reconstruction for dna sequencing by hybridization based on a constructive heuristic”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 4, pp. 1134–1140, 2011.
  54. [CHE 11b] CHEN Y.-C., CHAO K.-M., “On the generalized constrained longest common subsequence problems”, Journal of Combinatorial Optimization, vol. 21, no. 3, pp. 383–392, 2011.
  55. [CHI 94] CHIN F., POON C.K., “Performance analysis of some simple heuristics for computing longest common subsequences”, Algorithmica, vol. 12, nos 4–5, pp. 293–311, 1994.
  56. [CHI 04] CHIN F.Y.L., DESANTIS A., FERRARA A.L. et al., “A simple algorithm for the constrained sequence problems”, Information Processing Letters, vol. 90, no. 4, pp. 175–179, 2004.
  57. [CHR 04] CHROBAK M., KOLMAN P., SGALL J., “The greedy algorithm for the minimum common string partition problem”, in JANSEN K., KHANNA S., ROLIM J.D.P. et al. (eds), Proceedings of APPROX 2004 – 7th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, vol. 3122, Springer, 2004.
  58. [CLE 06] CLERC M. (ed), Particle Swarm Optimization, ISTE, London, 2006.
  59. [COR 07] CORMODE G., MUTHUKRISHNAN S., “The string edit distance matching problem with moves”, ACM Transactions on Algorithms, vol. 3, no. 2, pp. 1–19, 2007.
  60. [CRO 12] CROCE F.D., SALASSA F., “Improved LP-based algorithms for the closest string problem”, Computers & Operations Research, vol. 39, pp. 746–749, 2012.
  61. [CRO 14] CROCE F.D., GARRAFFA M., “The selective fixing algorithm for the closest string problem”, Computers & Operations Research, vol. 41, pp. 24–30, 2014.
  62. [DE 11] DE LEONE R., FESTA P., MARCHITTO E., “Solving a bus driver scheduling problem with randomized multistart heuristics”, International Transactions in Operational Research, vol. 18, no. 6, pp. 707–727, 2011.
  63. [DON 06] DONG Q.-W., LIN L., WANG X.-L. et al., “Contact-based simulated annealing protein sequence alignment method”, Proceedings of IEEE-EMBS 2005 – 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp. 2798–2801, 2006.
  64. [DOR 97] DORIGO M., GAMBARDELLA L.M., “Ant colony system: a cooperative learning approach to the traveling salesman problem”, IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53–66, 1997.
  65. [DOR 04] DORIGO M., STÜTZLE T., Ant Colony Optimization, MIT Press, Cambridge, MA, 2004.
  66. [DOR 10] DORIGO M., STÜTZLE T., “Ant colony optimization: overview and recent advances”, in GENDREAU M., POTVIN J.Y. (eds), Handbook of Metaheuristics, 2nd ed., Springer, 2010.
  67. [DRM 89] DRMANAC R., LABAT I., BRUKNER R. et al., “Sequencing of megabase plus DNA by hybridization: theory of the method”, Genomics, vol. 4, pp. 114–128, 1989.
  68. [EAS 07] EASTON T., SINGIREDDY A., “A spezialized branching and fathoming technique for the longest common subsequence problem”, International Journal of Operations Research, vol. 4, no. 2, pp. 98–104, 2007.
  69. [EAS 08] EASTON T., SINGIREDDY A., “A large neighborhood search heuristic for the longest common subsequence problem”, Journal of Heuristics, vol. 14, no. 3, pp. 271–283, 2008.
  70. [EDG 04a] EDGAR R.C., “MUSCLE: multiple sequence alignment with high accuracy and high throughput”, Nucleic Acids Research, vol. 32, no. 5, pp. 1792–1797, 2004.
  71. [EDG 04b] EDGAR R.C., “MUSCLE: a multiple sequence alignment method with reduced time and space complexity”, BMC Bioinformatics, vol. 5, no. 1, pp. 1–19, 2004.
  72. [EDG 06] EDGAR R.C., BATZOGLOU S., “Multiple sequence alignment”, Current Opinion in Structural Biology, vol. 16, no. 3, pp. 368–373, 2006.
  73. [EIB 91] EIBEN A.E., AARTS E.H.L., VAN HEE K.M., “Global convergence of genetic algorithms: a Markov chain analysis”, Proceedings of PPSN 1991 – 1st Workshop on Parallel Problem Solving from Nature, vol. 496, Springer, pp. 3–12, 1991.
  74. [EL 04] EL-MABROUK N., LABUDA D., “Haplotypes histories as pathways of recombinations”, Bioinformatics, vol. 20, no. 12, pp. 1836–1841, 2004.
  75. [END 04] ENDO T.A., “Probabilistic nucleotide assembling method for sequencing by hybridization”, Bioinformatics, vol. 20, no. 14, pp. 2181–2188, 2004.
  76. [ENG 05] ENGELBRECHT A.P., Fundamentals of Computational Swarm Intelligence, John Wiley and Sons, 2005.
  77. [ERE 05] EREL E., SABUNCUOGLU I., SEKERCI H., “Stochastic assembly line balancing using beam search”, International Journal of Production Research, vol. 43, no. 7, pp. 1411–1426, 2005.
  78. [FAN 05] FANG S.-C., WANG Y., ZHONG J., “A genetic algorithm approach to solving DNA fragment assembly problem”, Journal of Computational and Theoretical Nanoscience, vol. 2, no. 4, pp. 499–505, 2005.
  79. [FAR 05] FARIA JR H., BINATO S., RESENDE M.G.C. et al., “Transmission network design by a greedy randomized adaptive path relinking approach”, IEEE Transactions on Power Systems, vol. 20, no. 1, pp. 43–49, 2005.
  80. [FEL 68] FELLER W., An Introduction to Probability Theory and its Applications, 3rd ed., John Wiley and Sons, 1968.
  81. [FEO 89] FEO T.A., RESENDE M.G.C., “A probabilistic heuristic for a computationally difficult set covering problem”, Operations Research Letters, vol. 8, pp. 67–71, 1989.
  82. [FEO 95] FEO T.A., RESENDE M.G.C., “Greedy randomized adaptive search procedures”, Journal of Global Optimization, vol. 6, pp. 109–133, 1995.
  83. [FER 05] FERNANDES E.R., RIBEIRO C.C., “Using an adaptive memory strategy to improve a multistart heuristic for sequencing by hybridization”, in NIKOLETSEAS S.E. (ed), Proceedings of WEA 2005 – 4th International Workshop on Experimental and Efficient Algorithms, Springer-Verlag, Berlin, 2005.
  84. [FER 13a] FERDOUS S.M., SOHEL RAHMAN M., “Solving the minimum common string partition problem with the help of ants”, in TAN Y., SHI Y., MO H. (eds), Proceedings of ICSI 2013 – 4th International Conference on Advances in Swarm Intelligence, Springer, pp. 306–313, 2013.
  85. [FER 13b] FERONE D., FESTA P., RESENDE M.G.C., “Hybrid metaheuristics for the far from most string problem”, Proceedings of HM 2013 – 8th International Workshop on Hybrid Metaheuristics, vol. 7919, Springer, 2013.
  86. [FER 14] FERDOUS S.M., SOHEL RAHMAN M., “A MAX-MIN ant colony system for minimum common string partition problem”, CoRR, abs/1401.4539, available at: http://arxiv.org/abs/1401.4539, 2014.
  87. [FER 15] FERDOUS S.M., SOHEL RAHMAN M., “An integer programming formulation of the minimum common string partition problem”, Plos ONE, vol. 10, no. 7, p. e0130266, 2015.
  88. [FER 16] FERONE D., FESTA P., RESENDE M.G.C., “Hybridizations of grasp with path-relinking for the far from most string problem”, International Transactions in Operational Research, vol. 23, no. 3, pp. 481–506, 2016.
  89. [FES 02a] FESTA P., PARDALOS P.M., RESENDE M.G.C. et al., “Randomized heuristics for the MAX-CUT problem”, Optimization Methods and Software, vol. 7, pp. 1033–1058, 2002.
  90. [FES 02b] FESTA P., RESENDE M.G.C., “GRASP: an annotated bibliography”, in RIBEIRO C.C., HANSEN P. (eds), Essays and Surveys on Metaheuristics, Kluwer Academic Publishers, 2002.
  91. [FES 06] FESTA P., PARDALOS P.M., PITSOULIS L.S. et al., “GRASP with path-relinking for the weighted MAXSAT problem”, ACM Journal of Experimental Algorithmics, vol. 11, pp. 1–16, 2006.
  92. [FES 07] FESTA P., “On some optimization problems in molecular biology”, Mathematical Bioscience, vol. 207, no. 2, pp. 219–234, 2007.
  93. [FES 09a] FESTA P., RESENDE M.G.C., “An annotated bibliography of GRASP – Part I: algorithms”, International Transactions in Operational Research, vol. 16, no. 1, pp. 1–24, 2009.
  94. [FES 09b] FESTA P., RESENDE M.G.C., “An annotated bibliography of GRASP – Part II: applications”, International Transactions in Operational Research, vol. 16, no. 2, pp. 131–172, 2009.
  95. [FES 13] FESTA P., RESENDE M.G.C., “Hybridizations of GRASP with path-relinking”, Studies in Computational Intelligence, vol. 434, pp. 135–155, 2013.
  96. [FOG 62] FOGEL L.J., “Toward inductive inference automata”, Communications of the ACM, vol. 5, no. 6, pp. 319–319, 1962.
  97. [FOG 66] FOGEL L.J., OWENS A.J., WALSH M.J., Artificial Intelligence through Simulated Evolution, Wiley, 1966.
  98. [FRA 95] FRASER C.B., Subsequences and supersequences of strings, PhD Thesis, University of Glasgow, 1995.
  99. [FRA 97] FRANCES M., LITMAN A., “On covering problems of codes”, Theory of Computing Systems, vol. 30, no. 2, pp. 113–119, 1997.
  100. [GAR 79] GAREY M.R., JOHNSON D.S., Computers and Intractability: A Guide to the Theory of NP-Completeness, W.H. Freeman, New York, 1979.
  101. [GEN 10a] GENDREAU M., POTVIN J.-Y. (eds), Handbook of Metaheuristics, 2nd ed., Springer, 2010.
  102. [GEN 10b] GENDREAU M., POTVIN J.-Y., “Tabu search”, in Handbook of Metaheuristics, Springer, 2010.
  103. [GHI 05] GHIRARDI M., POTTS C.N., “Makespan minimization for scheduling unrelated parallel machines: a recovering beam search approach”, European Journal of Operational Research, vol. 165, no. 2, pp. 457–467, 2005.
  104. [GLO 96] GLOVER F., “Tabu search and adaptive memory programming – advances, applications and challenges”, in BARR R.S., HELGASON R.V., KENNINGTON J.L. (eds), Interfaces in Computer Science and Operations Research, Kluwer, 1996.
  105. [GLO 97] GLOVER F., LAGUNA M., Tabu Search, Kluwer Academic Publishers, 1997.
  106. [GLO 00a] GLOVER F., “Multi-start and strategic oscillation methods – principles to exploit adaptive memory”, LAGUNA M., GONZÁLES-VELARDE J.L. (eds), Computing Tools for Modeling, Optimization and Simulation: Interfaces in Computer Science and Operations Research, Kluwer, 2000.
  107. [GLO 00b] GLOVER F., LAGUNA M., MARTÍ R., “Fundamentals of scatter search and path relinking”, Control and Cybernetics, vol. 39, no. 3, pp. 653–684, 2000.
  108. [GOL 87] GOLDBERG D.E., SEGREST P., “Finite Markov chain analysis of genetic algorithms”, in Proceedings of the Second International Conference on Genetic Algorithms, 1987.
  109. [GOL 89] GOLDBERG D.E., Genetic Algorithms in Search, Optimization, and Learning, Addison-Wesley, Reading, MA, 1989.
  110. [GOL 05] GOLDSTEIN A., KOLMAN P., ZHENG J., “Minimum common string partition problem: hardness and approximations”, in FLEISCHER R., TRIPPEN G. (eds), Proceedings of ISAAC 2004 – 15th International Symposium on Algorithms and Computation, Springer, 2005.
  111. [GOL 11] GOLDSTEIN I., LEWENSTEIN M., “Quick greedy computation for minimum common string partitions”, in GIANCARLO R., MANZINI G. (eds), Proceedings of CPM 2011 – 22nd Annual Symposium on Combinatorial Pattern Matching, Springer, 2011.
  112. [GOM 58] GOMORY R.E., “Outline of an algorithm for integer solutions to linear programs”, Bulletin of the American Mathematical Society, vol. 64, pp. 275–278, 1958.
  113. [GOT 96] GOTOH O., “Significant improvement in accuracy of multiple protein sequence alignments by iterative refinement as assessed by reference to structural alignments”, Journal of Molecular Biology, vol. 264, no. 4, pp. 823–838, 1996.
  114. [GOT 08] GOTTHILF Z., HERMELIN D., LEWENSTEIN M., “Constrained LCS: hardness and approximation”, in FERRAGINA P., LANDAU G.M. (eds), Proceedings of CPM 2008 – 19th Annual Symposium on Combinatorial Pattern Matching, Springer, Berlin, 2008.
  115. [GRA 02] GRAMM J., HÜFFNER F., NIEDERMEIER R. et al., “Closest strings, primer design, and motif search”, Proceedings of RECOMB 2002 – Sixth Annual International Conference on Computational Molecular Biology, pp. 74–75, 2002.
  116. [GRA 03a] GRAMM J., Fixed-parameter algorithms for the consensus analysis of genomic data, PhD Thesis, University of Tübingen, Germany, 2003.
  117. [GRA 03b] GRAMM J., NIEDERMEIER R., ROSSMANITH P., “Fixed-parameter algorithms for closest string and related problems”, Algorithmica, vol. 37, pp. 25–42, 2003.
  118. [GUE 95] GUENOCHE A., VITTE P., “Longest common subsequence with many strings: exact and approximate methods”, Technique et science informatiques, vol. 14, no. 7, pp. 897–915, 1995, In French.
  119. [GUE 04] GUENOCHE A., “Supersequence of masks for oligo-chips”, Journal of Bioinformatics and Computational Biology, vol. 2, no. 3, pp. 459–469, 2004.
  120. [GUR 15] GUROBI OPTIMIZATION INC., Gurobi Optimizer Reference Manual, available at: https://www.gurobi.com/documentation/6.5/refman/refman.html, 2015.
  121. [GUS 93] GUSFIELD D., “Efficient methods for multiple sequence alignment with guaranteed error bounds”, Bulletin of Mathematical Biology, vol. 55, pp. 141–154, 1993.
  122. [GUS 97] GUSFIELD D., Algorithms on Strings, Trees, and Sequences, Cambridge University Press, 1997.
  123. [HAN 10] HANSEN P., MLADENOVIĆ N., BRIMBERG J. et al., “Variable neighborhood search”, Handbook of Metaheuristics, Springer, pp. 61–86, 2010.
  124. [HE 07] HE D., “A novel greedy algorithm for the minimum common string partition problem”, in MANDOIU I., ZELIKOVSKY A. (eds), Proceedings of ISBRA 2007 – Third International Symposium on Bioinformatics Research and Applications, Springer, 2007.
  125. [HER 00] HERTZ A., KOBLER D., “A framework for the description of evolutionary algorithms”, European Journal of Operational Research, vol. 126, pp. 1–12, 2000.
  126. [HIG 88] HIGGINS D.G., SHARP P.M., “Clustal: a package for performing multiple sequence alignment on a microcomputer”, Gene, vol. 73, pp. 237–244, 1988.
  127. [HIR 95] HIROSAWA M., TOTOKI Y., HOSHIDA M. et al., “Comprehensive study on iterative algorithms of multiple sequence alignment”, Computer Applications in the Biosciences, vol. 11, no. 1, pp. 13–18, 1995.
  128. [HOC 96] HOCHBAUM D. ed., Approximation Algorithms for NP-hard Problems, PWS Publishing, 1996.
  129. [HOL 75] HOLLAND J.H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.
  130. [HOO 15] HOOS H.H., STÜTZLE T., “Stochastic local search algorithms: an overview”, in Springer Handbook of Computational Intelligence, Springer, 2015.
  131. [HSU 84] HSU W.J., DU M.W., “Computing a longest common subsequence for a set of strings”, BIT Numerical Mathematics, vol. 24, no. 1, pp. 45–59, 1984.
  132. [HUA 04] HUANG K., YANG C., TSENG K., “Fast algorithms for finding the common subsequences of multiple sequences”, in Proceedings of the International Computer Symposium, IEEE Press, 2004.
  133. [HUA 15] HUANG K.-W., CHEN J.-L., YANG C.-S. et al., “A memetic particle swarm optimization algorithm for solving the DNA fragment assembly problem”, Neural Computing and Applications, vol. 26, no. 3, pp. 495–506, 2015.
  134. [HUA 16] HUANG K.-W., CHEN J.-L., YANG C.-S. et al., “A memetic gravitation search algorithm for solving DNA fragment assembly problems”, Journal of Intelligent & Fuzzy Systems, vol. 30, no. 4, pp. 2245–2255, 2016.
  135. [HUD 85] HUDSON R.R., KAPLAN N.L., “Statistical properties of the number of recombination events in the history of a sample of DNA sequences”, Genetics, vol. 111, pp. 147–164, 1985.
  136. [HUG 14] HUGHES J., HOUGHTEN S., MALLEN-FULLERTON G.M. et al., “Recentering and restarting genetic algorithm variations for DNA fragment assembly”, in Proceedings of the 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biolog, IEEE Press, 2014.
  137. [IBM 16] IBM CORPORATION., User’s Manual for CPLEX, available at: http://www.ibm.com/support/knowledgecenter/en/SSSA5P_12.6.3/ilog.odms.studio.help/Optimization_Studio/topics/PLUGINS_ROOT/ilog.odms.studio.help/pdf/usrcplex.pdf, 2016.
  138. [IDU 95] IDURY R.M., WATERMAN M.S., “A new algorithm for DNA sequence assembly”, Journal of Computational Biology, vol. 2, no. 2, pp. 291–306, 1995.
  139. [JAY 74] JAY E., BAMBARA R., PADMANABHAN R. et al., “DNA sequence analysis: a general, simple and rapid method for sequencing large oligodeoxyribonucleotide fragments by mapping”, Nucleic Acids Research, vol. 1, no. 3, pp. 331–354, 1974.
  140. [JIA 95] JIANG T., LI M., “On the approximation of shortest common supersequences and longest common subsequences”, SIAM Journal on Computing, vol. 24, no. 5, pp. 1122–1139, 1995.
  141. [JIA 02] JIANG T., LIN G., MA B. et al., “A general edit distance between RNA structures”, Journal of Computational Biology, vol. 9, no. 2, pp. 371–388, 2002.
  142. [JOR 15] JORDEHI A.R., JASNI J., “Particle swarm optimisation for discrete optimisation problems: a review”, Artificial Intelligence Review, vol. 43, no. 2, pp. 243–258, 2015.
  143. [KAP 06] KAPLAN H., SHAFRIR N., “The greedy algorithm for edit distance with moves”, Information Processing Letters, vol. 97, no. 1, pp. 23–27, 2006.
  144. [KAR 07] KARABOGA D., BASTURK B., “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm”, Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007.
  145. [KAR 08] KARABOGA D., BASTURK B., “On the performance of artificial bee colony (ABC) algorithm”, Applied Soft Computing, vol. 8, no. 1, pp. 687–697, 2008.
  146. [KEC 93] KECECIOGLU J., “The maximum weight trace problem in multiple sequence alignment”, in Proceedings of CPM 1993 – Annual Symposium on Combinatorial Pattern matching, Springer, 1993.
  147. [KEC 00] KECECIOGLU J., LENHOF H.-P., MEHLHORN K. et al., “A polyhedral approach to sequence alignment problems”, Discrete Applied Mathematics, vol. 104, pp. 143–186, 2000.
  148. [KEN 95] KENNEDY J., EBERHART R.C., “Particle swarm optimization”, Proceedings of the 1995 IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948, 1995.
  149. [KEN 04] KENNEDY J., EBERHART R.C., SHI Y., Swarm Intelligence, Morgan Kaufmann Publishers, San Francisco, CA, 2004.
  150. [KIK 06] KIKUCHI S., CHAKRABORTY G., “Heuristically tuned GA to solve genome fragment assembly problem”, Proceedings of CEC 2006 – IEEE Congress on Evolutionary Computation, IEEE Press, pp. 1491–1498, 2006.
  151. [KIR 83] KIRKPATRICK S., GELLAT C., VECCHI M., “Optimization by simulated annealing”, Science, vol. 220, pp. 671–680, 1983.
  152. [KLE 04] KLEINJUNG J., ROMEIN J., LIN K. et al., “Contact-based sequence alignment”, Nucleic Acids Research, vol. 32, pp. 2464–2473, 2004.
  153. [KOL 05] KOLMAN P., “Approximating reversal distance for strings with bounded number of duplicates”, in JEDRZEJOWICZ J., SZEPIETOWSKI A. (eds), Proceedings of MFCS 2005 – 30th International Symposium on Mathematical Foundations of Computer Science, Springer, 2005.
  154. [KOL 07] KOLMAN P., WALEŃ T., “Reversal distance for strings with duplicates: linear time approximation using hitting set”, in ERLEBACH T., KAKLAMANIS C. (eds), Proceedings of WAOA 2007 – 4th International Workshop on Approximation and Online Algorithms, Springer, 2007.
  155. [KOZ 92] KOZA J.R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
  156. [KOZ 99] KOZA J.R., BENNETT III F.H., ANDRE D. et al., Genetic Programming III, Darwinian Invention and Problem Solving, Morgan Kaufmann Publishers, 1999.
  157. [LAG 99] LAGUNA M., MARTÍ R., “GRASP and path relinking for 2-layer straight line crossing minimization”, INFORMS Journal on Computing, vol. 11, pp. 44–52, 1999.
  158. [LAL 15] LALWANI S., KUMAR R., GUPTA N., “A novel two-level particle swarm optimization approach for efficient multiple sequence alignment”, Memetic Computing, vol. 7, pp. 119–133, 2015.
  159. [LAM 12] LAM A.Y.S., LI V.O.K., “Chemical reaction optimization: a tutorial”, Memetic Computing, vol. 4, no. 1, pp. 3–17, 2012.
  160. [LAN 99] LANCTOT J.K., LI M., MA B. et al., “Distinguishing string selection problems”, Proceedings of SODA 1999 – Tenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1999.
  161. [LAN 01] LANDAU G.M., SCHMIDT J.P., SOKOL D., “An algorithm for approximate tandem repeat”, Journal of Computational Biology, vol. 8, no. 1, pp. 1–18, 2001.
  162. [LAN 03] LANCTOT J.K., LI M., MA B. et al., “Distinguishing string selection problems”, Information and Computation, vol. 185, no. 1, pp. 41–55, 2003.
  163. [LAN 04] LANCTOT J.K., Some string problems in computational biology, PhD Thesis, 2004.
  164. [LEC 01] LECOMPTE O., THOMPSON J.D., PLEWNIAK F. et al., “Multiple alignment of complete sequences (MACS) in the post-genomic era”, Gene, vol. 30, nos 1–2, pp. 17–30, 2001.
  165. [LEE 08] LEE Z.-J., SU S.-F., CHUANG C.-C. et al., “Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment”, Applied Soft Computing, vol. 8, pp. 55–78, 2008.
  166. [LI 99] LI M., MA B., WANG L., “Finding similar regions in many strings”, Proceedings of STOC 1999 – Thirty-first annual ACM symposium on Theory of computing, ACM, pp. 473–482, 1999.
  167. [LIU 05] LIU X., HE H., O.SÝKORA., “Parallel genetic algorithm and parallel simulated annealing algorithm for the closest string problem”, in LI X., WANG S., DONG Z.Y. (eds), Proceedings of ADMA 2005 – First International Conference on Advanced Data Mining and Applications, Springer, pp. 591–597, 2005.
  168. [LIU 11] LIU X., LIU S., HAO Z. et al., “Exact algorithm and heuristic for the closest string problem”, Computers & Operations Research, vol. 38, no. 11, pp. 1513–1520, 2011.
  169. [LIZ 15] LIZÁRRAGA E., BLESA M.J., BLUM C. et al., “On solving the most strings with few bad columns problem: an ILP model and heuristics”, in Proceedings of INISTA 2015 – International Symposium on Innovations in Intelligent SysTems and Applications, IEEE Press, pp. 1–8, 2015.
  170. [LIZ 16] LIZÁRRAGA E., BLESA M.J., BLUM C. et al., “Large neighborhood search for the most strings with few bad columns problem”, Soft Computing, 2016.
  171. [LÓP 11] LÓPEZ-IBÁNEZ M., DUBOIS-LACOSTE J., STÜTZLE T. et al., The irace package, iterated race for automatic algorithm configuration, Technical Report TR/IRIDIA/2011-004, IRIDIA, Université libre de Bruxelles, Belgium, 2011.
  172. [LOU 10] LOUREN ÇO H.R., MARTIN O.C., STÜTZLE T., “Iterated local search: framework and applications”, in Handbook of Metaheuristics, Springer, 2010.
  173. [LOZ 10] LOZANO M., BLUM C., “A hybrid metaheuristic for the longest common subsequence problem”, in BLESA M.J., BLUM C., RAIDL G. et al. (eds), Proceedings of HM 2010 – Proceedings of the 7th International Workshop on Hybrid Metaheuristics, Springer, Berlin, 2010.
  174. [LU 78] LU S.Y., FU K.S., “A sentence-to-sentence clustering procedure for pattern analysis”, IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 5, pp. 381–389, 1978.
  175. [LYN 05] LYNGSØR.B., SONG Y.S., “Minimum recombination histories by branch and bound”, in CASADIO R., MYERS G. (eds), Proceedings of WABI 2005 – Workshop on Algorithms in Bioinformatics, Springer Verlag, Berlin, 2005.
  176. [LYS 87] LYSOV Y.P., FLORENTIEV V.L., KHORLIN A.A. et al., “Determination of the nucleotide sequence of DNA using hybridization with oligonucleotides: a new method”, Doklady Akademii nauk SSSR, vol. 303, no. 6, pp. 1508–1511, 1987.
  177. [MA 08] MA B., SUN X., “More efficient algorithms for closest string and substring problems”, in VINGRON M., WONG L. (eds), Proceedings of RECOMB 2008 – 12th Annual International Conference on Research in Computational Molecular Biology, Springer, 2008.
  178. [MA 09] MA B., SUN X., “More efficient algorithms for closest string and substring problems”, SIAM Journal on Computing, vol. 39, no. 4, pp. 1432–1443, 2009.
  179. [MAC 90] MACARIO A.J.L., CONWAY DE MACARIO E., (eds), Gene Probes for Bacteria, San Diego Academic Press, 1990.
  180. [MAI 78] MAIER D., “The complexity of some problems on subsequences and supersequences”, Journal of the ACM, vol. 25, pp. 322–336, 1978.
  181. [MEH 15] MEHENNI T., “Multiple guide trees in a tabu search algorithm for the multiple sequence alignment problem”, in AMINE A. et al. (eds), Proceedings of the 5th IFIP TC 5 International Conference (CIIA 2015), Springer, 2015.
  182. [MEN 05] MENESES C.N., OLIVEIRA C.A.S., PARDALOS P.M., “Optimization techniques for string selection and comparison problems in genomics”, IEEE Engineering in Medicine and Biology Magazine, vol. 24, no. 3, pp. 81–87, 2005.
  183. [MET 53] METROPOLIS N., ROSENBLUTH A., ROSENBLUTH M. et al., “Equation of state calculations by fast computing machines”, Journal of Chemical Physics, vol. 21, pp. 1087–1092, 1953.
  184. [MOR 12] MORA-GUTIÉRREZ R.A., RAMÍREZ-RODRÍGUEZ J., RINCÓN-GARCÌA E.A. et al., “An optimization algorithm inspired by social creativity systems”, Computing, vol. 94, no. 11, pp. 887–914, 2012.
  185. [MOR 14a] MORA-GUTIÉRREZ R.A., RAMÍREZ-RODRÍGUEZ J., RINCÓN-GARCÌA E.A., “An optimization algorithm inspired by musical composition”, Artificial Intelligence Review, vol. 41, no. 3, pp. 301–315, 2014.
  186. [MOR 14b] MORÁN-MIRABAL L.F., GONZÁLEZ-VELARDE J.L., RESENDE M.G.C., “Randomized heuristics for the family traveling salesperson problem”, International Transactions in Operational Research, vol. 21, no. 1, pp. 41–57, 2014.
  187. [MOR 15] MORA-GUTIÉRREZ R.A., LÁRRAGA-RAMÍREZ M.E., RINCÓN-GARCÌA E.A. et al., “Adaptation of the method of musical composition for solving the multiple sequence alignment problem”, Computing, vol. 97, pp. 813–842, 2015.
  188. [MOU 12a] MOUSAVI S.R., BABAIE M., MONTAZERIAN M., “An improved heuristic for the far from most strings problem”, Journal of Heuristics, vol. 18, pp. 239–262, 2012.
  189. [MOU 12b] MOUSAVI S.R., TABATABA F., “An improved algorithm for the longest common subsequence problem”, Computers & Operations Research, vol. 39, no. 3, pp. 512–520, 2012.
  190. [NAZ 12] NAZNIN F., SARKER R., ESSAM D., “Progressive alignment method using genetic algorithm for multiple sequence alignment”, IEEE Transactions on Evolutionary Computation, vol. 16, no. 5, pp. 615–631, 2012.
  191. [NEB 08] NEBRO A.J., LUQUE G., LUNA F. et al., “DNA fragment assembly using a gridbased genetic algorithm”, Computers & Operations Research, vol. 35, no. 9, pp. 2776–2790, 2008.
  192. [NEE 70] NEEDLEMAN S.B., WUNSCH C.D., “A general method applicable to the search for similarities in the amino acid sequence of two proteins”, Journal of Molecular Biology, vol. 48, pp. 443–453, 1970.
  193. [NEM 88] NEMHAUSER G.L., WOLSEY L.A., Integer and Combinatorial Optimization, John Wiley and Sons, 1988.
  194. [NIK 10] NIKOLAEV A.G., JACOBSON S.H., “Simulated annealing”, in Handbook of Metaheuristics, Springer, 2010.
  195. [NOT 96] NOTREDAME C., HIGGINS D.G., “SAGA: sequence alignment by genetic algorithm”, Nucleic Acids Research, vol. 24, no. 8, pp. 1515–1524, 1996.
  196. [NOT 00] NOTREDAME C., HIGGINS D.G., HERINGA J., “T-Coffee: a novel method for fast and accurate multiple sequence alignment”, Journal of Molecular Biology, vol. 302, pp. 205–217, 2000.
  197. [OW 88] OW P.S., MORTON T.E., “Filtered beam search in scheduling”, International Journal of Production Research, vol. 26, pp. 297–307, 1988.
  198. [PAP 82] PAPADIMITRIOU C.H., STEIGLITZ K., Combinatorial Optimization – Algorithms and Complexity, Dover Publications, NY, 1982.
  199. [PAP 13] PAPPALARDO E., PARDALOS P.M., STRACQUADANIO G., Optimization Approaches for Solving String Selection Problems, Springer, NY, 2013.
  200. [PAR 95a] PARSONS R.J., JOHNSON M.E., “DNA sequence assembly and genetic algorithms – new results and puzzling insights”, Proceedings ISMB – Third International Conference on Intelligent Systems for Molecular Biology, pp. 277–84, 1995.
  201. [PAR 95b] PARSONS R.J., FORREST S., BURKS C., “Genetic algorithms, operators, and DNA fragment assembly”, Machine Learning, vol. 21, no. 1–2, pp. 11–33, 1995.
  202. [PAR 04] PARDALOS P.M., OLIVEIRA C.A.S., LU Z. et al., “Optimal solutions for the closest string problem via integer programming”, INFORMS Journal on Computing, vol. 16, pp. 419–429, 2004.
  203. [PEV 89] PEVZNER P.A., “l-tuple DNA sequencing: Computer analysis”, Journal of Biomulecular Structure and Dynamics, vol. 7, pp. 63–73, 1989.
  204. [PEV 00] PEVZNER P., Computational Molecular Biology: An Algorithmic Approach, MIT Press, 2000.
  205. [PIS 10] PISINGER D., ROPKE S., “Large neighborhood search”, in GENDREAU M., POTVIN J.-Y. (eds), Handbook of Metaheuristics, Springer, 2010.
  206. [PIT 12] PITZER E., AFFENZELLER M., Recent Advances in Intelligent Engineering Systems, Springer, 2012.
  207. [RAJ 01a] RAJASEKARAN S., NICK H., PARDALOS P.M. et al., “Efficient algorithms for local alignment search”, Journal of Combinatorial Optimization, vol. 5, no. 1, pp. 117–124, 2001.
  208. [RAJ 01b] RAJASEKARAN S., HU Y., LUO J. et al., “Efficient algorithms for similarity search”, Journal of Combinatorial Optimization, vol. 5, no. 1, pp. 125–132, 2001.
  209. [RAS 07] RASTAS P., UKKONEN E., “Haplotype inference via hierarchical genotype parsing”, in GIANCARLO R., HANNENHALLI S. (eds), Proceedings of WABI2007 – 7th Workshop on Algorithms in Bioinformatics, Springer, 2007.
  210. [REC 73] RECHENBERG I., Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Frommann-Holzboog, 1973.
  211. [REI 73] REICHERT T.A., COHEN D.N., WONG A.K.C., “An application of information theory to genetic mutations and the matching of polypeptide sequences”, Journal of Theoretical Biology, vol. 42, no. 2, pp. 245–261, 1973.
  212. [REI 97] REINELT K., LENHOF H.-P., MUTZEL P. et al., “A branch-and-cut algorithm for multiple sequence alignment”, in Proceedings of RECOMB 1997 – Annual International Conference of Computational Molecular Biology, ACM, pp. 241–249, 1997.
  213. [RES 04] RESENDE M.G.C., WERNECK R.F., “A hybrid heuristic for the p-median problem”, Journal of Heuristics, vol. 10, pp. 59–88, 2004.
  214. [RES 10a] RESENDE M.G.C., RIBEIRO C.C., “Greedy randomized adaptive search procedures: Advances, hybridizations, and applications”, in Handbook of Metaheuristics, Springer, 2010.
  215. [RES 10b] RESENDE M.G.C., MARTÍ R., GALLEGO M. et al., “GRASP and path relinking for the max-min diversity problem”, Computers & Operations Research, vol. 37, pp. 498–508, 2010.
  216. [RIA 04] RIAZ T., WANG Y., LI K.-B., “Multiple sequence alignment using tabu search”, in AMINE A. et al. (eds), Proceedings of APBC 2004 – Second conference on Asia-Pacific Bioinformatics, vol. 29, Australian Computer Society, pp. 223–232, 2004.
  217. [RIB 07] RIBEIRO C.C., ROSSETI I., “Efficient parallel cooperative implementations of GRASP heuristics”, Parallel Computing, vol. 33, pp. 21–35, 2007.
  218. [RIB 12] RIBEIRO C.C., RESENDE M.G.C., “Path-relinking intensification methods for stochastic local search algorithms”, Journal of Heuristics, vol. 18, pp. 193–214, 2012.
  219. [ROL 09] ROLI A., BLUM C., “Tabu search for the founder sequence reconstruction problem: a preliminary study”, in OMATU S., ROCHA M.P., BRAVO J. et al. (eds), Proceedings of IWPACBB 2009 – 3rd International Workshop on Practical Applications of Computational Biology and Bioinformatics, vol. 5518, Springer Verlag, Berlin, 2009.
  220. [ROL 12] ROLI A., BENEDETTINI S., STÜTZLE T. et al., “Large neighbourhood search algorithms for the founder sequence reconstruction problem”, Computers & Operations Research, vol. 39, no. 2, pp. 213–224, 2012.
  221. [ROM 92] ROMAN S., Coding and Information Theory, Springer-Verlag, 1992.
  222. [RUB 77] RUBIN S.M., REDDY R., “The locus model of search and its use in image interpretation”, REDDY R. (ed), in Proceedings of IJCAI 1977 – 5th International Joint Conference on Artificial Intelligence, vol. 2, William Kaufmann, pp. 590–595, 1977.
  223. [SAB 99] SABUNCUOGLU I., BAYIZ M., “Job shop scheduling with beam search”, European Journal of Operational Research, vol. 118, no. 2, pp. 390–412, 1999.
  224. [SAN 72] SANKOFF D., “Matching sequences under deletion-insertion constraints”, Proceedings of the National Academy of Sciences of the United States of America, vol. 69, no. 1, pp. 4–6, 1972.
  225. [SAN 83] SANKOFF D., KRUSKAL J.B., Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, Addison-Wesley, Reading, UK, 1983.
  226. [SCH 02] SCHWARTZ R., CLARK A., ISTRAIL S., “Methods for inferring block-wise ancestral history from haploid sequences”, Proceedings of WABI 2002 – Workshop on Algorithms in Bioinformatics, Springer Verlag, Berlin, 2002.
  227. [SEL 74] SELLERS P.H., “On the theory and computation of evolutionary distances”, SIAM Journal of Applied Mathematics, vol. 26, no. 4, pp. 787–793, 1974.
  228. [SEL 88] SELLIS T., “Multiple query optimization”, ACM Transactions on Database Systems, vol. 13, no. 1, pp. 23–52, 1988.
  229. [SHA 02] SHAPIRA D., STORER J.A., “Edit distance with move operations”, in APOSTOLICO A., TAKEDA M. (eds), Proceedings of CPM 2002 – 13th Annual Symposium on Combinatorial Pattern Matching, Lecture Notes in Computer Science, vol. 2373, Springer, pp. 85–98, 2002.
  230. [SHY 09] SHYU S.J., TSAI C.-Y., “Finding the longest common subsequence for multiple biological sequences by ant colony optimization”, Computers & Operations Research, vol. 36, no. 1, pp. 73–91, 2009.
  231. [SIM 99] SIM J.S., PARK K., “The consensus string problem for a metric is NP-complete”, Proceedings of AWOCA 1999 – Annual Australiasian Workshop on Combinatorial Algorithms, pp. 107–113, 1999.
  232. [SIM 03] SIM J.S., PARK K., “The consensus string problem for a metric is NP-complete”, Journal of Discrete Algorithms, vol. 1, no. 1, pp. 111–117, 2003.
  233. [SIN 07] SINGIREDDY A., Solving the longest common subsequence problem in bioinformatics, Thesis, Kansas State University, 2007.
  234. [SMI 81] SMITH T., WATERMAN M., “Identification of common molecular subsequences”, Journal of Molecular Biology, vol. 147, no. 1, pp. 195–197, 1981.
  235. [STA 79] STADEN R., “A strategy of DNA sequencing employing computer programs”, Nucleic Acids Research, vol. 6, no. 7, pp. 2601–2610, 1979.
  236. [STO 88] STORER J., Data Compression: Methods and Theory, Computer Science Press, MD, 1988.
  237. [STÜ 00] STÜTZLE T., HOOS H.H., “MAX-MIN ant system”, Future Generation Computer Systems, vol. 16, no. 8, pp. 889–914, 2000.
  238. [SUN 14] SUN J., PALADE V., WU X. et al., “Multiple sequence alignment with hidden markov models learned by random drift particle swarm optimization”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. 1, pp. 243–257, 2014.
  239. [TAI 91] TAILLARD É.D., “Robust Taboo Search for the Quadratic Assignment Problem”, Parallel Computing, vol. 17, pp. 443–455, 1991.
  240. [TAN 12] TANAKA S., “A heuristic algorithm based on lagrangian relaxation for the closest string problem”, Computers & Operations Research, vol. 39, pp. 709–717, 2012.
  241. [THO 94] THOMPSON J.D., HIGGINS D.G., GIBSON T.J., “CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice”, Nucleic Acids Research, vol. 22, no. 8, pp. 4673–4680, 1994.
  242. [THY 04] THYSON G.W., CHAPMAN J., HUGENHOLTZ P. et al., “Community structure and metabolism through reconstruction of microbial genomes from the environment”, Nature, vol. 428, no. 6978, pp. 37–43, 2004.
  243. [TSA 03] TSAI Y.-T., “The constrained longest common subsequence problem”, Information Processing Letters, vol. 88, no. 4, pp. 173–176, 2003.
  244. [UKK 02] UKKONEN E., “Finding founder sequences from a set of recombinants”, in GUIGÓ R., GUSFIELD D. (eds), Proceedings of the 2nd Workshop on Algorithms in Bioinformatics – WABI 2002, Springer Verlag, Berlin, Germany, pp. 277–286, 2002.
  245. [VAZ 01] VAZIRANI V., Approximation Algorithms, Springer-Verlag, 2001.
  246. [VOß 05] VOSS S., FINK A., DUIN C., “Looking ahead with the pilot method”, Annals of Operations Research, vol. 136, no. 1, pp. 285–302, 2005.
  247. [WAL 05] WALLACE I.M., O’SULLIVAN O., HIGGINS D.G., “Evaluation of iterative alignment algorithms for multiple alignment”, Bioinformatics, vol. 21, no. 8, pp. 1408–1414, 2005.
  248. [WAN 94] WANG L., JIANG T., “On the complexity of multiple sequence alignment”, Journal of Computational Biology, vol. 1, pp. 337–348, 1994.
  249. [WAN 07] WANG F., LIM A., “A stochastic beam search for the berth allocation problem”, Decision Support Systems, vol. 42, no. 4, pp. 2186–2196, 2007.
  250. [WAN 09] WANG L., ZHU B., “Efficient algorithms for the closest string and distinguishing string selection problems”, in DENG X., HOPCROFT J.E., XUE J. (eds), Proceedings of FAW 2009 – Third International Workshop on Frontiers in Algorithmics, Springer, pp. 261–270, 2009.
  251. [WAT 76] WATERMAN M.S., SMITH T.F., BEYER W.A., “Some biological sequence metrics”, Advances in Mathematics, vol. 20, no. 3, pp. 367–387, 1976.
  252. [WIL 11] WILLIAMSON D.P., SHMOYS D.B., The Design of Approximation Algorithms, Cambridge University Press, 2011.
  253. [WU 99] WU B.Y., LANCIA G., BAFNA V. et al., “A polynomial-time approximation scheme for minimum routing cost spanning trees”, SIAM Journal on Computing, vol. 29, no. 3, pp. 761–778, 1999.
  254. [WU 08] WU Y., GUSFIELD D., “Improved algorithms for inferring the minimum mosaic of a set of recombinants”, Proceedings of CPM 2007 – Proceedings of the 18th Annual Symposium on Combinatorial Pattern Matching, Springer Verlag, Berlin, 2008.
  255. [WU 13] WU J., WANG H., “A parthenogenetic algorithm for the founder sequence reconstruction problem”, Journal of Computers, vol. 8, no. 11, pp. 2934–2941, 2013.
  256. [ZHA 08] ZHAO Y., MA P., LAN J. et al., “An improved ant colony algorithm for DNA sequence alignment”, Proceedings of ISISE 2008 – International Symposium on Information Science and Engineering, IEEE Press, 2008.
  257. [ZHA 09] ZHANG Q., WANG W., MCMILLAN L. et al., “Inferring genome-wide mosaic structure”, Bioinformatics, pp. 150–161, 2009.
  258. [ZHU 15] ZHU D., WU Y., WANG X., “A dynamic programming algorithm for a generalized LCS problem with multiple subsequence inclusion constraints”, in HSU C.-H., XIA F., LIU X. et al. (eds), Proceedings of IOV 2015 – Second International Conference on Internet of Vehicles - Safe and Intelligent Mobility, Springer, 2015.
  259. [ZÖR 11] ZÖRNIG P., “Improved optimization modelling for the closest string and related problems”, Applied Mathematical Modelling, vol. 35, no. 12, pp. 5609–5617, 2011.
  260. [ZÖR 15] ZÖRNIG P., “Reduced-size integer linear programming models for string selection problems: application to the farthest string problem”, Journal of Computational Biology, vol. 22, no. 8, pp. 729–742, 2015.
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
18.220.229.97