Preferences-based choice prediction in evolutionary multi-objective optimization

Manish Aggarwal; Justin Heinermann; Stefan Oehmcke; Oliver Kramer

doi:10.1007/978-3-319-55849-3_46

Preferences-based choice prediction in evolutionary multi-objective optimization

Manish Aggarwal, Justin Heinermann, Stefan Oehmcke, Oliver Kramer^*

^*Corresponding author af dette arbejde

2 Citationer (Scopus)

Abstract

Evolutionary multi-objective algorithms (EMOAs) of the type of NSGA-2 approximate the Pareto-front, after which a decisionmaker (DM) is confounded with the primary task of selecting the best solution amongst all the equally good solutions on the Pareto-front. In this paper, we complement the popular NSGA-2 EMOA by posteriori identifying a DM’s best solution among the candidate solutions on the Pareto-front, generated through NSGA-2. To this end, we employ a preference-based learning approach to learn an abstract ideal reference point of the DM on the multi-objective space, which reflects the compromises the DM makes against a set of conflicting objectives. The solution that is closest to this reference-point is then predicted as the DM’s best solution. The pairwise comparisons of the candidate solutions provides the training information for our learning model. The experimental results on ZDT1 dataset shows that the proposed approach is not only intuitive, but also easy to apply, and robust to inconsistencies in the DM’s preference statements.

Originalsprog	Engelsk
Titel	Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings
Redaktører	J.Ignacio Hidalgo, Carlos Cotta, Ting Hu, Alberto Tonda, Paolo Burrelli, Matt Coler, Giovanni Iacca, Michael Kampouridis, Antonio M. Mora Garcia, Giovanni Squillero, Anthony Brabazon, Evert Haasdijk, Jacqueline Heinerman, Fabio D Andreagiovanni, Jaume Bacardit, Trung Thanh Nguyen, Sara Silva, Ernesto Tarantino, Anna I. Esparcia-Alcazar, Gerd Ascheid, Kyrre Glette, Stefano Cagnoni, Paul Kaufmann, Francisco Fernandez de Vega, Michalis Mavrovouniotis, Mengjie Zhang, Federico Divina, Kevin Sim, Neil Urquhart, Robert Schaefer
Antal sider	10
Forlag	Springer Verlag,
Publikationsdato	1 jan. 2017
Sider	715-724
ISBN (Trykt)	9783319558486
DOI	https://doi.org/10.1007/978-3-319-55849-3_46
Status	Udgivet - 1 jan. 2017
Begivenhed	20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017 - Amsterdam, Holland Varighed: 19 apr. 2017 → 21 apr. 2017

Konference

Konference	20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017
Land/Område	Holland
By	Amsterdam
Periode	19/04/2017 → 21/04/2017

Navn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind	10199 LNCS
ISSN	0302-9743

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10.1007/978-3-319-55849-3_46

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Aggarwal, M., Heinermann, J., Oehmcke, S., & Kramer, O. (2017). Preferences-based choice prediction in evolutionary multi-objective optimization. I J. I. Hidalgo, C. Cotta, T. Hu, A. Tonda, P. Burrelli, M. Coler, G. Iacca, M. Kampouridis, A. M. Mora Garcia, G. Squillero, A. Brabazon, E. Haasdijk, J. Heinerman, F. D Andreagiovanni, J. Bacardit, T. T. Nguyen, S. Silva, E. Tarantino, A. I. Esparcia-Alcazar, G. Ascheid, K. Glette, S. Cagnoni, P. Kaufmann, F. F. de Vega, M. Mavrovouniotis, M. Zhang, F. Divina, K. Sim, N. Urquhart, ... R. Schaefer (red.), Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings (s. 715-724). Springer Verlag,. https://doi.org/10.1007/978-3-319-55849-3_46

Preferences-based choice prediction in evolutionary multi-objective optimization. / Aggarwal, Manish; Heinermann, Justin; Oehmcke, Stefan et al.
Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings. red. / J.Ignacio Hidalgo; Carlos Cotta; Ting Hu; Alberto Tonda; Paolo Burrelli; Matt Coler; Giovanni Iacca; Michael Kampouridis; Antonio M. Mora Garcia; Giovanni Squillero; Anthony Brabazon; Evert Haasdijk; Jacqueline Heinerman; Fabio D Andreagiovanni; Jaume Bacardit; Trung Thanh Nguyen; Sara Silva; Ernesto Tarantino; Anna I. Esparcia-Alcazar; Gerd Ascheid; Kyrre Glette; Stefano Cagnoni; Paul Kaufmann; Francisco Fernandez de Vega; Michalis Mavrovouniotis; Mengjie Zhang; Federico Divina; Kevin Sim; Neil Urquhart; Robert Schaefer. Springer Verlag, 2017. s. 715-724 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 10199 LNCS).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Aggarwal, M, Heinermann, J, Oehmcke, S & Kramer, O 2017, Preferences-based choice prediction in evolutionary multi-objective optimization. i JI Hidalgo, C Cotta, T Hu, A Tonda, P Burrelli, M Coler, G Iacca, M Kampouridis, AM Mora Garcia, G Squillero, A Brabazon, E Haasdijk, J Heinerman, F D Andreagiovanni, J Bacardit, TT Nguyen, S Silva, E Tarantino, AI Esparcia-Alcazar, G Ascheid, K Glette, S Cagnoni, P Kaufmann, FF de Vega, M Mavrovouniotis, M Zhang, F Divina, K Sim, N Urquhart & R Schaefer (red), Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings. Springer Verlag, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 10199 LNCS, s. 715-724, 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, Amsterdam, Holland, 19/04/2017. https://doi.org/10.1007/978-3-319-55849-3_46

Aggarwal M, Heinermann J, Oehmcke S, Kramer O. Preferences-based choice prediction in evolutionary multi-objective optimization. I Hidalgo JI, Cotta C, Hu T, Tonda A, Burrelli P, Coler M, Iacca G, Kampouridis M, Mora Garcia AM, Squillero G, Brabazon A, Haasdijk E, Heinerman J, D Andreagiovanni F, Bacardit J, Nguyen TT, Silva S, Tarantino E, Esparcia-Alcazar AI, Ascheid G, Glette K, Cagnoni S, Kaufmann P, de Vega FF, Mavrovouniotis M, Zhang M, Divina F, Sim K, Urquhart N, Schaefer R, red., Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings. Springer Verlag,. 2017. s. 715-724. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 10199 LNCS). doi: 10.1007/978-3-319-55849-3_46

Aggarwal, Manish ; Heinermann, Justin ; Oehmcke, Stefan et al. / Preferences-based choice prediction in evolutionary multi-objective optimization. Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings. red. / J.Ignacio Hidalgo ; Carlos Cotta ; Ting Hu ; Alberto Tonda ; Paolo Burrelli ; Matt Coler ; Giovanni Iacca ; Michael Kampouridis ; Antonio M. Mora Garcia ; Giovanni Squillero ; Anthony Brabazon ; Evert Haasdijk ; Jacqueline Heinerman ; Fabio D Andreagiovanni ; Jaume Bacardit ; Trung Thanh Nguyen ; Sara Silva ; Ernesto Tarantino ; Anna I. Esparcia-Alcazar ; Gerd Ascheid ; Kyrre Glette ; Stefano Cagnoni ; Paul Kaufmann ; Francisco Fernandez de Vega ; Michalis Mavrovouniotis ; Mengjie Zhang ; Federico Divina ; Kevin Sim ; Neil Urquhart ; Robert Schaefer. Springer Verlag, 2017. s. 715-724 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 10199 LNCS).

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title = "Preferences-based choice prediction in evolutionary multi-objective optimization",

abstract = "Evolutionary multi-objective algorithms (EMOAs) of the type of NSGA-2 approximate the Pareto-front, after which a decisionmaker (DM) is confounded with the primary task of selecting the best solution amongst all the equally good solutions on the Pareto-front. In this paper, we complement the popular NSGA-2 EMOA by posteriori identifying a DM{\textquoteright}s best solution among the candidate solutions on the Pareto-front, generated through NSGA-2. To this end, we employ a preference-based learning approach to learn an abstract ideal reference point of the DM on the multi-objective space, which reflects the compromises the DM makes against a set of conflicting objectives. The solution that is closest to this reference-point is then predicted as the DM{\textquoteright}s best solution. The pairwise comparisons of the candidate solutions provides the training information for our learning model. The experimental results on ZDT1 dataset shows that the proposed approach is not only intuitive, but also easy to apply, and robust to inconsistencies in the DM{\textquoteright}s preference statements.",

keywords = "Multi-objective optimization, NSGA-2, Preference-based learning, Solution selection",

author = "Manish Aggarwal and Justin Heinermann and Stefan Oehmcke and Oliver Kramer",

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AU - Heinermann, Justin

AU - Oehmcke, Stefan

AU - Kramer, Oliver

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N2 - Evolutionary multi-objective algorithms (EMOAs) of the type of NSGA-2 approximate the Pareto-front, after which a decisionmaker (DM) is confounded with the primary task of selecting the best solution amongst all the equally good solutions on the Pareto-front. In this paper, we complement the popular NSGA-2 EMOA by posteriori identifying a DM’s best solution among the candidate solutions on the Pareto-front, generated through NSGA-2. To this end, we employ a preference-based learning approach to learn an abstract ideal reference point of the DM on the multi-objective space, which reflects the compromises the DM makes against a set of conflicting objectives. The solution that is closest to this reference-point is then predicted as the DM’s best solution. The pairwise comparisons of the candidate solutions provides the training information for our learning model. The experimental results on ZDT1 dataset shows that the proposed approach is not only intuitive, but also easy to apply, and robust to inconsistencies in the DM’s preference statements.

AB - Evolutionary multi-objective algorithms (EMOAs) of the type of NSGA-2 approximate the Pareto-front, after which a decisionmaker (DM) is confounded with the primary task of selecting the best solution amongst all the equally good solutions on the Pareto-front. In this paper, we complement the popular NSGA-2 EMOA by posteriori identifying a DM’s best solution among the candidate solutions on the Pareto-front, generated through NSGA-2. To this end, we employ a preference-based learning approach to learn an abstract ideal reference point of the DM on the multi-objective space, which reflects the compromises the DM makes against a set of conflicting objectives. The solution that is closest to this reference-point is then predicted as the DM’s best solution. The pairwise comparisons of the candidate solutions provides the training information for our learning model. The experimental results on ZDT1 dataset shows that the proposed approach is not only intuitive, but also easy to apply, and robust to inconsistencies in the DM’s preference statements.

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A2 - Iacca, Giovanni

A2 - Kampouridis, Michael

A2 - Mora Garcia, Antonio M.

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A2 - Kaufmann, Paul

A2 - de Vega, Francisco Fernandez

A2 - Mavrovouniotis, Michalis

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A2 - Divina, Federico

A2 - Sim, Kevin

A2 - Urquhart, Neil

A2 - Schaefer, Robert

PB - Springer Verlag,

T2 - 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017

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ER -

Preferences-based choice prediction in evolutionary multi-objective optimization

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