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

Datamanagement & Biometrics

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

2 Citations (Scopus)

49 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 '22
Subtitle of host publication	Proceedings of the Genetic and Evolutionary Computation Conference Companion
Pages	356-359
Number of pages	4
ISBN (Electronic)	978-1-4503-9268-6
DOIs	https://doi.org/10.1145/3520304.3528998
Publication status	Published - 9 Jul 2022

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title = "On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems",

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.",

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On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems. / Rook, Jeroen ; Trautmann, Heike; Bossek, Jakob et al.
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion. 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.

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

Abstract

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