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2018 | OriginalPaper | Chapter

Indicator-Based Versus Aspect-Based Selection in Multi- and Many-Objective Biochemical Optimization

Authors : Susanne Rosenthal, Markus Borschbach

Published in: Bioinspired Optimization Methods and Their Applications

Publisher: Springer International Publishing

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Abstract

The identification of qualified peptides as ligands for diagnostic and therapeutic interventions requires the solution of multi- and many-objective biochemical optimization problems. A MOEA has been designed for molecular optimization with a combined indicator- and Pareto-based selection strategy that encounters common classification problems of the solutions’ quality with the rise of the problem dimension. Therefore, a sophisticated selection strategy is presented in this work that selects the individuals for the succeeding generation related to two general aspects in biochemical optimization: the first aspect reflects the peptide quality and the second one the genetic dissimilarity among the peptides in a population. The search behavior of this aspect-based selection is compared to the traditional selection on generic 3- to 6-dimensional physiochemical optimization problems and the impact of the reference point in the aspect-based selection is investigated.

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Literature
1.
Otvos, L.: Peptide-Based Drug Design: Methods and Protocols. Humana Press Inc., New York (2000)
2.
3.
Rosenthal, S., Borschbach, M.: Design perspectives of an evolutionary process for multi-objective molecular optimization. In: Trautmann, H., Rudolph, G., Klamroth, K., Schütze, O., Wiecek, M., Jin, Y., Grimme, C. (eds.) EMO 2017. LNCS, vol. 10173, pp. 529–544. Springer, Cham (2017). https://​doi.​org/​10.​1007/​978-3-319-54157-0_​36CrossRef
4.
Rosenthal, S., Borschbach, M.: General aspect-based selection concept for multi- and many-objective molecular optimization. In: International Conference on Genetic and Evolutionary Computation (GECCO 2017), pp. 45–46 (2017)
5.
Ishibuchi, H., Akedo, N., Nojima, Y.: Behavior of multiobjective evolutionary algorithms on many-objective knapsack problems. IEEE Trans. Evol. Comput. 19(2), 264–283 (2015)CrossRef
6.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRef
7.
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRef
8.
Bader, J., Zitzler, E.: HypE: analgorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)CrossRef
9.
Bandyopadhyay, S., Mukherjee, A.: An algorithm for many-objective optimization with reduced objective computations: a study in differential evolution. IEEE Trans. Evol. Comput. 19(3), 400–413 (2015)CrossRef
10.
11.
Wang, R., Purshouse, R., Giagkiozis, I., Fleming, P.: The iPICEA-g: a new hybrid evolutionary multi-criteria decision making approach using the brushing technique. Eur. J. Oper. Res. 243(2), 442–453 (2015)MathSciNetCrossRef
12.
Wang, R., Purshouse, R., Fleming, P.: Preference-inspired coevolutionary algorithms for many-objective optimization. IEEE Trans. Evol. Comput. 17(4), 474–494 (2013)CrossRef
13.
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multiobjective optimization. Evol. Comput. 10(3), 263–282 (2002)CrossRef
14.
Zou, X., Chen, Y., Liu, M., Kang, L.: A new evolutionary algorithm for solving many-objective optimization problems. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 38(5), 1402–1412 (2008)CrossRef
15.
Wang, G., Jiang, H.: Fuzzy-dominance and its application in evolutionary many objective optimization. In: International Conference on Computational Intelligence and Security Workshops (CISW 2007), pp. 195–198. IEEE (2007)
16.
Yang, S., Li, M., Liu, X., Zheng, J.: A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 17(5), 721–736 (2013)CrossRef
17.
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 557–601 (2014)CrossRef
18.
Li, B., Li, J., Tang, K., Yao, X.: Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 48(1), 13 (2015)CrossRef
19.
20.
Emmerich, M., Lee, B., Render, A.: Analyzing molecular landscapes using random walks and information theory. Chem. Cent. J. 3(1), 20 (2009)CrossRef
21.
Hopp, T., Woods, K.: A computer program for predicting protein antigenic determinants. Mol. Immunol. 20(4), 483–489 (1983)CrossRef
22.
Guruprasad, K., Reddy, B., Pandit, M.: Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary structure. Protein Eng. 4(2), 155–161 (1990)CrossRef
23.
Needleman, S., Wunsch, C.: A general method application to the research for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)CrossRef
24.
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998). https://​doi.​org/​10.​1007/​BFb0056872CrossRef
Metadata
Title
Indicator-Based Versus Aspect-Based Selection in Multi- and Many-Objective Biochemical Optimization
Authors
Susanne Rosenthal
Markus Borschbach
Copyright Year
2018
Book
Bioinspired Optimization Methods and Their Applications

Print ISBN: 978-3-319-91640-8

Electronic ISBN: 978-3-319-91641-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-91641-5_22

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