Publikationsserver der Universitätsbibliothek Marburg

Titel:A Multi-objective Genetic Algorithm for Peptide Optimization
Autor:Rosenthal, Susanne
Weitere Beteiligte: Freisleben, Bernd (Prof. Dr.)
Veröffentlicht:2016
URI:https://archiv.ub.uni-marburg.de/diss/z2016/0862
DOI: https://doi.org/10.17192/z2016.0862
URN: urn:nbn:de:hebis:04-z2016-08626
DDC: Informatik
Titel (trans.):Ein multi-objektiver Algorithmus zur Peptidoptimierung
Publikationsdatum:2016-12-06
Lizenz:https://rightsstatements.org/vocab/InC-NC/1.0/

Dokument

Schlagwörter:
Peptidoptimierung, Metaheuristik, Landschaftsanalyse, Multi-objektiver genetischer Algorithmus, peptide optimization, multi-objective genetic algorithm, landscape analysis

Summary:
The peptide-based drug design process requires the identification of a wide range of candidate molecules with specific biological, chemical and physical properties. The laboratory analysis in terms of in vitro methods for the discovery of several physiochemical properties of theoretical candidate molecules is time- and cost-intensive. Hence, in silico methods are required for this purpose. Metaheuristics like evolutionary algorithms are considered to be adequate in silico methods providing good approximate solutions to the underlying multiobjective optimization problems. The general issue in this area is the design of a multi-objective evolutionary algorithm to achieve a maximum number of high-quality candidate peptides that differ in their genetic material, in a minimum number of generations. A multi-objective evolutionary algorithm as an in silico method of discovering a large number of high-quality peptides within a low number of generations for a broad class of molecular optimization problems of different dimensions is challenging, and the development of such a promising multi-objective evolutionary algorithm based on theoretical considerations is the major contribution of this thesis. The design of this algorithm is based on a qualitative landscape analysis applied on a three- and four-dimensional biochemical optimization problem. The conclusions drawn from the empirical landscape analysis of the three- and four-dimensional optimization problem result in the formulation of hypotheses regarding the types of evolutionary algorithm components which lead to an optimized search performance for the purpose of peptide optimization. Starting from the established types of variation operators and selection strategies, different variation operators and selection strategies are proposed and empirically verified on the three- and four-dimensional molecular optimization problem with regard to an optimized interaction and the identification of potential interdependences as well as a fine-tuning of the parameters. Moreover, traditional issues in the field of evolutionary algorithms such as selection pressure and the influence of multi-parent recombination are investigated.

Bibliographie / References

  1. [69] Frey, A. Schlussbericht zum Forschungsvorhaben 'Evolvierung von Peptidliganden und Erforschung intelligenter Sonden für die optische Bildgebung' im Forschungsverbund 'Optischen Sonden für die medizinische Diagnostik und die zellbiologische Forschung': OPTOPROBE. Technische Informationsbibliothek und Universitätsbibiliothek, Hannover, DOI: 10.2314/GBV:813174007 (2013).
  2. [19] Blickle, T., and Thiele, L. A Comparison of Selection Schemes used in Genetic Algorithms. TIK-Report, Zürich (1995).
  3. [86] Hopp, T., and Woods, K. A Computer Programm for Predicting Protein Antigenic Determinants. Mol. Immunology 20, 4 (1983), 483- 489.
  4. [84] Holland, J. Adaption in Natural and Artificial Systems. Ann Arbor: University of Michigan Press (1975), 287-299.
  5. [13] Baker, J. Adaptive Selection Methods for Genetic Algorithms. In Proceedings of an International Conference on Genetic Algorithms (1985), 101-111.
  6. [93] Kita, H., and Yamamura, M. A Functional Specialization Hypothesis for Designing Genetic Algorithms. In Proc. of the IEEE Int. Conf. on systems, Man, Cypernetics (1999), 579-584.
  7. [88] Hortijk, W. A Measure of Landscapes. Evolutionary Computation 4, 4 (1996), 335-360.
  8. [4] Babbar, A., Lakshmikantha, A., and Goldberg, D. A Modified NSGA-II to Solve Noisy Multiobjective Problems. In Proceedings of the Genetic and Evolutionary Computation Conference, AAAI (2003), 21- 27.
  9. [83] Hohm, T., Limbourg, P., and Hoffmann, D. A multi-objective Evolutionary Algorithm for the Design of Peptide Mimotopes. Journal of Computational Biology 13, 1 (2006), 113-125.
  10. [55] Emmerich, M., Lee, B., Render, A., Faddiev, E., Kruisselbrink, J., Deutz, A., van der Horst, E., IJzerman, A., and Bäck, T. Analyzing Molecular Landscapes Using Random Walks and Information Theory. Chemistry Central Journal 3, 1 (2009), 20.
  11. [54] Emmerich, M., Beume, N., and Naujoks, B. An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. EMO 2005, LNCS 3410 (2005), 62-76.
  12. [52] Eiben, A., and Bäck, T. An Empirical Investigation of Multi-parent Recombination Operators in Evolutionary Strategies. Evolutionary Computation 5, 3 (1997), 347-365.
  13. [87] Horn, J., Nafpliotis, N., and Goldberg, D. A Niched Pareto Genetic Algorithm for Multiobjective Optimization. IEEE International Conference on Evolutionary Computation 1 (1994), 82-87.
  14. [67] Fonseca, C., Paquete, L., and Lopez-Ibanez, M. An Improved Dimension-Sweep Algorithm for the Hypervolume Indicator. In 2006 IEEE Congress on Evolutionary Computation (CEC'2006) (2006), 3973- 3979.
  15. [12] Bäck, T., and Schwefel, H.-P. An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation 1, 1 (1993), 1-23.
  16. [9] Bäck, T. An Overview of Parameter Control Methods by Self-Adaption in Evolutionary Algorithms. Fandamenta informaticae 34, 1-15 (1998).
  17. [120] Ono, I., and Kobayashi, S. A Real-coded Genetic Algorithm for Functional Optimization using Unimodal Normal Distribution Crossover. In Proceedings of th 7th International Conference on Genetic Algorithms (ICGA-7) (1997), 246-253.
  18. [1] Aggarwal, s., Garg, R., and Goswami, P. A Review Paper on Different Encoding Schemes used in Genetic Algorithms. International Journal of Advanced Research in Computer Science and Software Engineering 4, 1 (2014), 596-600.
  19. [64] Fogel, L., Owens, A., and Walsh, M. Artificial intelligence through simulated evolution. John Wiley, 1966.
  20. [29] Chan, T. A Slightly Faster Algorithm for Klee's Measure Problem. Computational Geometry: Theory and Applications 43, 243-250 (2010).
  21. [58] Eshelman, L., Caruana, R., and D., S. Biases in the Crossover Landscape. Proc. 3rd. Int. Conf. on Genetic Algorithms, ed. J.D. Schaffer 3 (1989), 10-19.
  22. [123] Prlic, A., Yates, A., and Bliven, S. BioJava: an open-source framework for bioinformatics in 2012. Bioinformatics 28, 20 (2012), 2693-2695.
  23. [28] Ceperly, D., Chen, Y., Crain, R., Meng, X., Mira, A., and Rosenthal, J. Challenges and Advances in High Dimensional and High Complexity Monte Carlo Computation and Theory. Workshop, Banff International Research Station for Mathematical Innovation Discovery, 2012.
  24. [56] Engelbrecht, A. Computational Intelligence: An Introduction. John Wiley& Sons, 2007.
  25. [59] Eshelman, L., Mathias, K., and Schaffer, J. Crossover Operator Biases: Exploiting the Population Distribution. Proc. ICGA 97 (1997), 354-361.
  26. [61] Fahrmeier, L., Künstler, R., Pigeot, I., and Tutz, G. Statistik: Der Weg zur Datenanalyse. Berlin; Springer, 1999.
  27. [8] Bäck, T. Evolutionary Algorithms in Theory and Praxis. New York: Oxford Univ. Press, 1996.
  28. [105] Leier, A., and Banzhaf, W. Exploring the Search Space of Quantum Programs. In Proceedings of the 2003 Congress on Evolutionary Computation IEEE Press 1 (2003), 170-177.
  29. [10] Bäck, T., and Hoffmeister, F. Extended Selection Mechanisms in Genetic Algorithms. ICGA4 (1991), 92-99.
  30. [90] Jones, T., and Forrest, S. Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithm. In Proceedings of the 6th International Conference on Genetic Algorithms (1995), 184-192.
  31. [128] Reeves, C. Fitness landscapes. Search Methodologies, E. Burke and G. Kendall, Eds. Springer, 2005, pp. 587-610.
  32. [126] Razali, N., and Geraghty, J. Genetic Algorithm Performance with Different Selection Strategies in Solving TSP. Proceedings of the World Congress on Engineering, WCE 2011 II (2011).
  33. [65] Fonseca, C., and Fleming, P. Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. Proc. of the Fifth Int. Conf. on Genentic Algorithms, (Forrest Ed.) (1993), 416-423.
  34. [53] Eiben, A., Raué, P.-E., and Ruttkay, Z. Genetic Algorithms with Multi-Parent Recombination. In Proceedings of the third Conference on Parallel Problem Solving from Nature - PPSNIII 866 (1994), 78-87.
  35. [89] Jones, G. Genetic and Evolutionary Algorithms. Encyclopedia of Computational Chemistry, John Wiley & Sons, Ltd., 1998.
  36. [26] Carvalho, A., and A.F., A. Improving NSGA-II with an Adaptive Mutation Operator. Proc. of the 11th Annual Conference Companion on Genetic and Evolutionary Computational Conference, GECCO'09 (2009), 2697-2700.
  37. [11] Bäck, T., and Schütz, M. Intelligent Mutation Rate Control in Canonical Genetic Algorithm. Proc. of the International Symposium on Methodology for Intelligent Systems (1996), 158-167.
  38. [116] Nicolaou, C., Brown, N., and Pattichis, C. Molecular Optimization using Computational Multi-objective Methods. Drug Discovery & Development 10(3) (2007), 316-324.
  39. [24] Cardoso, R., da Cruz, A., Wanner, E., and Takahashi, R. Multi-objective Evolutionary Optimization of Biological Pest Control with Impulsive Dynamics in Soybean Crops. Bulletin of Mathematical Biology 71, 1463- 1481 (2009).
  40. [70] Garrett, D., and Dasgupta, D. Multiobjective Landscape Analysis and the Generalized Assignment Problem. Springer-Verlang, Berlin, Heidelberg, 2008, pp. 110-124.
  41. [66] Fonseca, C., and Fleming, P. Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms - Part ii: Application Example. IEEE Trans. Syst. Man, Cybern. A. 28 (1998), 38-47.
  42. [118] Oduguwa, A., Tiwari, A., Fiorentino, S., and Roy, R. MultiObjective Optimization of the Protein-Ligand Docking Problem in Drug Discovery. Genetic and Evolutionary Computation Conference, GECCO 2006 (2006), 1793-1800.
  43. [117] Ochoa, G., Harvey, I., and Buxton, H. On Recombination and Optimal Mutation Rates. In Proc. of Genetic and Evolutionary Computation Conference (GECCO 1999) (1999), 488-495.
  44. [16] Beume, N., Fonseca, C., Lopez-Ibanez, M., Paquete, L., and Vahrenhold, J. On the Complexity of Computing the Hypervolume Indicator. IEEE Transactions on Evolutionary Computation 13, 5 (2009), 1075-1082.
  45. [3] Albuquerque, P., Chopard, B., and Mazza, C. On the Impact of the Representation on Fitness Landscape. Proc. of the Third International Conference on Genetic Programming (2000), 1-15.
  46. [23] Brockhoff, D., Wagner, T., and Trautmann, H. On the Properties of the R2 Indicator. In Genetic and Evolutionary Computation Conference (GECCO 2012) (2012), 465-472.
  47. [122] Picek, S., Jakobovic, D., and Golub, M. On the Recombination Operator in the Real-Coded Genetic Algorithm. In the Proceedings of the IEEE Congress on Evolutionary Computation (CEC) (2013), 3103-3110.
  48. [6] Bäck, T. Optimal Mutation Rates in Genetic Research. Proc. of the Fifth International Conference on Genetic Algorithms (1993), 2-8.
  49. [121] Otvos, L., Ed. Peptide-based Drug Design. Springer Verlag, Berlin, 2010.
  50. [27] Castranho, M., and Santos, N. Peptide Drug Discovery and Development. Wiley-VCH, 2011.
  51. [92] Khare, V., Yao, X., and Deb, K. Performance Scaling of Multiobjective Evolutionary Algorithms. Proc. of the Second Int. Conf. on Evolutionary Multi-Criterion Optimization, EMO 2003 2632, 376-390 (2003).
  52. [15] Bechikh, S., Belgasim, N., and Ben Said, L. Ghédira, K. PHCNSGA-II: A Novel Multi-objective Memetic Algorithm for Continuous Optimization. Proc. of the 20th Int. Conf. on Tools with Artifical Intelligence, IEEE (2008), 180-189.
  53. [71] Garrett, D., and Dasgupta, D. Plateau Connection Structure and Multiobjective Metaheuristic Performance. Congress on Evolutionary Computation, (CEC 2009) (2009), 1281-1288.
  54. [85] Hopp, T., and Woods, K. Prediction of Protein Antigenic Determinants from Amino Acids Sequences. In Proc. Natl. Acad. Sci USA 78 (1981), 3824-3828.
  55. [60] Eshelman, L., and Schaffer, J. Real-coded Genetic Algorithms and Interval Schemata. In D. Whitley (Ed.), Foundation of genetic Algorithm II (1993), 187-202.
  56. [14] Baker, J. Reducing Bias and Inefficiency in the Selection Algorithm. Proceedings of the Second International Conference on Genetic Algorithms and their Application (Hillsdale, New Jersey: L. Erlbaum Associates) (1987), 14-21.
  57. [168] Wagner, T., Trautmann, H., and Brockhoff, D. Reference Articulation by Means of the R2 Indicator. In Evolutionary Multi-criterion Optimization (EMO 2013) 7811 (2013), 81-95.
  58. [25] Caruana, R., Eshelman, L., and D., S. Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover. In 11-th Int. Joint Conf. on Artificial Intelligence 1 (1989), 750-755.
  59. [68] Fortin, F.-A., and Parizeau, M. Revisiting the NSGA-II CrowdingDistance Computation. Proc. of the 15th Annual conference in Genetic and Evolutionary Computation, GECCO '13 (2013), 623-630.
  60. [7] Bäck, T. Selective Pressure in Evolutionary Algorithms: A Characterization of Selection Mechanisms. First IEEE Eonference on Evolutionary Computing 1 (1994), 57-62.
  61. [17] Beume, N., Naujoks, B., and Emmerich, M. SMS-EMOA: Multiobjective Selection Based on Dominated Hypervolume. European Journal of Operation Research 181 (3) (2007), 1653-1669.
  62. [148] Solaro, R., Chiellini, F., and Battisti, A. Targeted Delivery of Proteins Drugs by Nanocarriers. Materials 3 (2010), 1928-1980.
  63. [18] Bhardwaj, A., Leelavathi, S., Mazumdar-Leighton, S., Gosh, A., Ramakumar, S., and Reddy, V. The Critical Role of N- and C-Terminal Contact in Protein Stability and Folding of a Family 10 Xylanase under Extreme Conditions. PLos ONE 5, 6 (2010), e11347.
  64. [5] Bäck, T. The Interaction of Mutation Rates, Selection and Selfadaption within a Genetic Algorithm. Proc. of the 2nd Parallel Problem Solving from Nature (1992), 85-94.
  65. [57] Erickson, M., Mayer, A., and Horn, J. The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems. Proceedings of the 1st International Conference on Evolutionary Multi-Criterion Optimization EMO 2001 Lecture Notes in Computer Science 1993 (2001), 681-695.
  66. [30] Coello Coello, C., and Cruz Cortis, N. Solving Multiobjecti[32] Corne, D., Knowles, J., and Oates, M. The Pareto Envelopebased Selection Algorithm for Multiobjective Optimization. Proceedings of Parallel Problem Solving from Nature PPSN VI 1917 (2000), 839-848.
  67. Trans. on Evolutionary Computation 16, 3 (2008), 355-384.
  68. [91] Kershenbaum, A. When Genetic Algorithms Work Best. Journal of Computing, INFORMS 9, 3 (1997), 254-255.
  69. [2] Aguirre, H., and Tanaka, K. Working Principles, Behavior, and Performance of MOEAs on MNK-Landscapes. European Journal of Operational Research 181, 3 (2007), 1670-1690.


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