Set-SMAA for finding preferable multi-objective solutions

Rotem Dror; Amir Kantor; Michael Masin; Segev Shlomov

doi:10.1145/3067695.3076005

Set-SMAA for finding preferable multi-objective solutions

Rotem Dror
, Amir Kantor
, Michael Masin
, Segev Shlomov

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Multi-objective optimization problems involve more than one objective to be optimized simultaneously. Typically however, there is no single solution that simultaneously optimizes them all. This can be overcome by calculating a set of compromise solutions, called the Pareto frontier. We hereby introduce a new approach, called Set-SMAA. It extends stochastic multi-criteria acceptability analysis (SMAA) to find a small set of preferable solutions from the frontier. This applies to diverse use cases, from decision-making and product recommendation, to evolutionary multi-objective optimization algorithms and their fitness measures.

Original language	English
Title of host publication	GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference
Publisher	Association for Computing Machinery
Pages	149-150
Number of pages	2
ISBN (Electronic)	9781450349208
DOIs	https://doi.org/10.1145/3067695.3076005
State	Published - 1 Jul 2017
Externally published	Yes
Event	2017 Genetic and Evolutionary Computation Conference, GECCO 2017 - Berlin, Germany Duration: 15 Jul 2017 → 19 Jul 2017

Publication series

Name	GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference

Conference

Conference	2017 Genetic and Evolutionary Computation Conference, GECCO 2017
Country/Territory	Germany
City	Berlin
Period	15/07/17 → 19/07/17

Bibliographical note

Publisher Copyright:
© 2017 Owner/Author.

Keywords

decision-making
fitness evaluation
multi-objective optimization
recommender systems
stochastic multi-criteria acceptability analysis

ASJC Scopus subject areas

Software
Computer Science Applications
Computational Theory and Mathematics

Access to Document

10.1145/3067695.3076005

Cite this

Dror, R., Kantor, A., Masin, M., & Shlomov, S. (2017). Set-SMAA for finding preferable multi-objective solutions. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 149-150). (GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference). Association for Computing Machinery. https://doi.org/10.1145/3067695.3076005

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abstract = "Multi-objective optimization problems involve more than one objective to be optimized simultaneously. Typically however, there is no single solution that simultaneously optimizes them all. This can be overcome by calculating a set of compromise solutions, called the Pareto frontier. We hereby introduce a new approach, called Set-SMAA. It extends stochastic multi-criteria acceptability analysis (SMAA) to find a small set of preferable solutions from the frontier. This applies to diverse use cases, from decision-making and product recommendation, to evolutionary multi-objective optimization algorithms and their fitness measures.",

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author = "Rotem Dror and Amir Kantor and Michael Masin and Segev Shlomov",

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doi = "10.1145/3067695.3076005",

language = "English",

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Dror, R, Kantor, A, Masin, M & Shlomov, S 2017, Set-SMAA for finding preferable multi-objective solutions. in GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference. GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, pp. 149-150, 2017 Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, 15/07/17. https://doi.org/10.1145/3067695.3076005

Set-SMAA for finding preferable multi-objective solutions. / Dror, Rotem; Kantor, Amir; Masin, Michael et al.
GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, 2017. p. 149-150 (GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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KW - fitness evaluation

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KW - recommender systems

KW - stochastic multi-criteria acceptability analysis

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M3 - Conference contribution

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EP - 150

BT - GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference

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T2 - 2017 Genetic and Evolutionary Computation Conference, GECCO 2017

Y2 - 15 July 2017 through 19 July 2017

ER -

Set-SMAA for finding preferable multi-objective solutions

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this