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2024 | OriginalPaper | Buchkapitel

On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems

verfasst von : Oliver Ludger Preuß, Jeroen Rook, Heike Trautmann

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer Nature Switzerland

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Abstract

The complexity of Multi-Objective (MO) continuous optimisation problems arises from a combination of different characteristics, such as the level of multi-modality. Earlier studies revealed that there is a conflict between solver convergence in objective space and solution set diversity in the decision space, which is especially important in the multi-modal setting. We build on top of this observation and investigate this trade-off in a multi-objective manner by using multi-objective automated algorithm configuration (MO-AAC) on evolutionary multi-objective algorithms (EMOA). Our results show that MO-AAC is able to find configurations that outperform the default configuration as well as configurations found by single-objective AAC in regards to objective space convergence and diversity in decision space, leading to new recommendations for high-performing default settings.

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Vorheriges Kapitel A New Angle: On Evolving Rotation Symmetric Boolean Functions

Nächstes Kapitel A Simple Statistical Test Against Origin-Biased Metaheuristics

Source code: https://github.com/jeroenrook/SMAC3/tree/mosmac-anon.

Experimental code can be found at https://github.com/jeroenrook/MMMOO-moconfig-exp.

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Titel: On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems
verfasst von: Oliver Ludger Preuß
Jeroen Rook
Heike Trautmann
Verlag: Springer Nature Switzerland
Buch: Applications of Evolutionary Computation
Print ISBN: 978-3-031-56851-0

Electronic ISBN: 978-3-031-56852-7

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-56852-7_20

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