On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems

Jeroen Rook; Heike Trautmann; Jakob Bossek; Christian Grimme

doi:10.1145/3520304.3528998

On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems

Jeroen Rook
, Heike Trautmann
, Jakob Bossek
, Christian Grimme

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

7 Citations (Scopus)

225 Downloads (Pure)

Abstract

Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.

Original language	English
Title of host publication	GECCO 2022 Companion
Subtitle of host publication	Proceedings of the 2022 Genetic and Evolutionary Computation Conference
Place of Publication	New York, NY
Publisher	Association for Computing Machinery
Pages	356-359
Number of pages	4
ISBN (Electronic)	978-1-4503-9268-6
DOIs	https://doi.org/10.1145/3520304.3528998 https://doi.org/10.1145/3520304.3528998
Publication status	Published - 9 Jul 2022
Event	Genetic and Evolutionary Computation Conference, GECCO 2022 - Boston, United States Duration: 9 Jul 2022 → 13 Jul 2022 https://gecco-2022.sigevo.org/HomePage

Conference

Conference	Genetic and Evolutionary Computation Conference, GECCO 2022
Abbreviated title	GECCO 2022
Country/Territory	United States
City	Boston
Period	9/07/22 → 13/07/22
Internet address	https://gecco-2022.sigevo.org/HomePage

Keywords

Configuration
Multi-modality
Multi-objective optimization
2024 OA procedure

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ArticleFinal published version, 153 KBLicence: Taverne

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Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems. In GECCO 2022 Companion: Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 356-359). Association for Computing Machinery. https://doi.org/10.1145/3520304.3528998, https://doi.org/10.1145/3520304.3528998

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abstract = "Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.",

keywords = "Configuration, Multi-modality, Multi-objective optimization, 2024 OA procedure",

author = "Jeroen Rook and Heike Trautmann and Jakob Bossek and Christian Grimme",

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year = "2022",

month = jul,

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doi = "10.1145/3520304.3528998",

language = "English",

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Rook, J, Trautmann, H, Bossek, J & Grimme, C 2022, On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems. in GECCO 2022 Companion: Proceedings of the 2022 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, New York, NY, pp. 356-359, Genetic and Evolutionary Computation Conference, GECCO 2022, Boston, Massachusetts, United States, 9/07/22. https://doi.org/10.1145/3520304.3528998, https://doi.org/10.1145/3520304.3528998

On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems. / Rook, Jeroen; Trautmann, Heike; Bossek, Jakob et al.
GECCO 2022 Companion: Proceedings of the 2022 Genetic and Evolutionary Computation Conference. New York, NY: Association for Computing Machinery, 2022. p. 356-359.

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

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AB - Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.

KW - Configuration

KW - Multi-modality

KW - Multi-objective optimization

KW - 2024 OA procedure

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ER -

On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems

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Keywords

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