INSPM: An interactive evolutionary multi-objective algorithm with preference model
In this paper an interactive method for modeling the preferences of a Decision-Maker (DM) is employed to guide a modified version of the NSGA-II algorithm: the Interactive Non-dominated Sorting algorithm with Preference Model (INSPM). The INSPM’s task is to find a non-uniform sampling of the Pareto-...
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| Published in: | Information sciences Vol. 268; pp. 202 - 219 |
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01.06.2014
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| Abstract | In this paper an interactive method for modeling the preferences of a Decision-Maker (DM) is employed to guide a modified version of the NSGA-II algorithm: the Interactive Non-dominated Sorting algorithm with Preference Model (INSPM). The INSPM’s task is to find a non-uniform sampling of the Pareto-optimal front with a detailed sampling of the DM’s preferred regions and a coarse sampling of the non-preferred regions. In the proposed technique, a Radial Basis Function (RBF) network is employed to construct a function which represents the DM’s utility function using ordinal information only, extracted from queries to the DM. The INSPM algorithm calls the DM’s preference model via a Dynamic Crowding Distance (DCD) density control method which provides the mechanism for increasing the sampling in the preferred regions and for decreasing it in non-preferred regions which allows a fine-tunning control of the Pareto-optimal front sampling density. |
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| AbstractList | In this paper an interactive method for modeling the preferences of a Decision-Maker (DM) is employed to guide a modified version of the NSGA-II algorithm: the Interactive Non-dominated Sorting algorithm with Preference Model (INSPM). The INSPM’s task is to find a non-uniform sampling of the Pareto-optimal front with a detailed sampling of the DM’s preferred regions and a coarse sampling of the non-preferred regions. In the proposed technique, a Radial Basis Function (RBF) network is employed to construct a function which represents the DM’s utility function using ordinal information only, extracted from queries to the DM. The INSPM algorithm calls the DM’s preference model via a Dynamic Crowding Distance (DCD) density control method which provides the mechanism for increasing the sampling in the preferred regions and for decreasing it in non-preferred regions which allows a fine-tunning control of the Pareto-optimal front sampling density. |
| Author | Takahashi, Ricardo H.C. Pedro, Luciana R. |
| Author_xml | – sequence: 1 givenname: Luciana R. surname: Pedro fullname: Pedro, Luciana R. organization: Electrical Engineering, Universidade Federal de Minas Gerais, Brazil – sequence: 2 givenname: Ricardo H.C. surname: Takahashi fullname: Takahashi, Ricardo H.C. email: taka@mat.ufmg.br organization: Dep. of Mathematics, Universidade Federal de Minas Gerais, Brazil |
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| Cites_doi | 10.1109/4235.996017 10.1162/106365600568202 10.2307/2332226 10.1145/1143997.1144112 10.1109/TEVC.2010.2064323 10.1007/978-3-642-19893-9_38 10.1109/TEVC.2010.2070070 10.1007/978-3-642-37140-0_60 10.1109/CEC.2000.870313 |
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| References_xml | – reference: K. Deb, J. Sundar, Reference point based multi-objective optimization using evolutionary algorithms, in: GECCO, 2006, pp. 635–642. – start-page: 580 year: 2008 end-page: 585 ident: b0055 article-title: Dynamic crowding distance – a new diversity maintenance strategy for MOEAs publication-title: Proceedings of the 2008 Fourth International Conference on Natural Computation – vol. 01, ICNC ’08 – volume: 8 start-page: 173 year: 2000 end-page: 195 ident: b0070 article-title: Comparison of multiobjective evolutionary algorithms: Empirical results publication-title: Evol. Comput. – reference: The MathWorks Inc. Matlab – Version 6.9.0 (R2009b). The MathWorks Inc., Natick, Massachusetts, 2009. – volume: 14 start-page: 702 year: 2010 end-page: 722 ident: b0050 article-title: An interactive territory defining evolutionary algorithm: iTDEA publication-title: Trans. Evol. Comput. – volume: 7 start-page: 205 year: 1995 end-page: 230 ident: b0020 article-title: An overview of evolutionary algorithms in multiobjective optimization publication-title: Evol. Comput. – reference: L.R. Pedro, R.H.C. Takahashi, Modeling decision-maker preferences through utility function level sets, in: 6th International Conference on Evolutionary Multi-criterion Optimization, vol. 1, 2011. – reference: J.D. Knowles, D.W. Corne, M-paes: a memetic algorithm for multiobjective optimization, in Proceedings of the IEEE Congress on Evolutionary Computation, 2000, pp. 325–332. – year: 1976 ident: b0030 article-title: Decisions with Multiple Objectives: Preferences and Value Tradeoffs – volume: 30 start-page: 81 year: 1938 end-page: 93 ident: b0035 article-title: A new measure of rank correlation publication-title: Biometrika – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: b0005 article-title: A fast and elitist multiobjective genetic algorithm: NSGA II publication-title: IEEE Trans. Evol. Comput. – volume: vol. 3 year: 1998 ident: b0045 article-title: The Art of Computer Programming publication-title: Sorting and Searching – reference: L.R. Pedro, R.H.C. Takahashi, Decision-maker preference modeling in interactive multiobjective optimization, in: 7th International Conference on Evolutionary Multi-criterion Optimization, vol. 7811, 2013. – reference: E. Zitzler, M. Laumanns, L. Thiele, SPEA 2: Improving the strength Pareto evolutionary algorithms, in EUROGEN 2001, Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, 2002, pp. 95–100. – volume: 14 start-page: 723 year: 2010 end-page: 739 ident: b0010 article-title: An interactive evolutionary multiobjective optimization method based on progressively approximated value functions publication-title: Trans. Evol. Comput. – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.ins.2013.12.045_b0005 article-title: A fast and elitist multiobjective genetic algorithm: NSGA II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – year: 1976 ident: 10.1016/j.ins.2013.12.045_b0030 – volume: 7 start-page: 205 issue: 3 year: 1995 ident: 10.1016/j.ins.2013.12.045_b0020 article-title: An overview of evolutionary algorithms in multiobjective optimization publication-title: Evol. Comput. – volume: 8 start-page: 173 year: 2000 ident: 10.1016/j.ins.2013.12.045_b0070 article-title: Comparison of multiobjective evolutionary algorithms: Empirical results publication-title: Evol. Comput. doi: 10.1162/106365600568202 – ident: 10.1016/j.ins.2013.12.045_b0025 – volume: 30 start-page: 81 issue: 1/2 year: 1938 ident: 10.1016/j.ins.2013.12.045_b0035 article-title: A new measure of rank correlation publication-title: Biometrika doi: 10.2307/2332226 – volume: vol. 3 year: 1998 ident: 10.1016/j.ins.2013.12.045_b0045 article-title: The Art of Computer Programming – ident: 10.1016/j.ins.2013.12.045_b0015 doi: 10.1145/1143997.1144112 – ident: 10.1016/j.ins.2013.12.045_b0075 – volume: 14 start-page: 723 issue: 5 year: 2010 ident: 10.1016/j.ins.2013.12.045_b0010 article-title: An interactive evolutionary multiobjective optimization method based on progressively approximated value functions publication-title: Trans. Evol. Comput. doi: 10.1109/TEVC.2010.2064323 – start-page: 580 year: 2008 ident: 10.1016/j.ins.2013.12.045_b0055 article-title: Dynamic crowding distance – a new diversity maintenance strategy for MOEAs – ident: 10.1016/j.ins.2013.12.045_b0060 doi: 10.1007/978-3-642-19893-9_38 – volume: 14 start-page: 702 issue: 5 year: 2010 ident: 10.1016/j.ins.2013.12.045_b0050 article-title: An interactive territory defining evolutionary algorithm: iTDEA publication-title: Trans. Evol. Comput. doi: 10.1109/TEVC.2010.2070070 – ident: 10.1016/j.ins.2013.12.045_b0065 doi: 10.1007/978-3-642-37140-0_60 – ident: 10.1016/j.ins.2013.12.045_b0040 doi: 10.1109/CEC.2000.870313 |
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| SubjectTerms | Evolutionary computation Interactive algorithm Multi-objective optimization Progressive preference RBF network Utility function model |
| Title | INSPM: An interactive evolutionary multi-objective algorithm with preference model |
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