Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation-Reference-Cited by-同舟云学术

Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation

Published:2024-07-14 Issue: Volume:9 Page:23-31
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Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Rodriguez Cedric¹²^ORCID,Thomson Sarah³^ORCID,Alderliesten Tanja¹^ORCID,Bosman Peter⁴⁵^ORCID

Affiliation:

1. Leiden University Medical Center, Leiden, Netherlands

2. Centrum Wiskunde & Informatica (CWI), Leiden, Netherlands

3. Edinburgh Napier University, Edinburgh, United Kingdom

4. Centrum Wiskunde & Informatica (CWI), Amsterdam, Netherlands

5. Delft University of Technology, Amsterdam, Netherlands

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Publisher

ACM

Reference42 articles.

1. Local optima networks for continuous fitness landscapes

2. Hanan Alsouly, Michael Kirley, and Mario Andrés Muñoz. 2023. Dynamic landscape analysis for constrained multi-objective pptimization problems. In Australasian Joint Conference on Artificial Intelligence. Springer, 429--441.

3. Samineh Bagheri, Wolfgang Konen, and Thomas Bäck. 2016. Online selection of surrogate models for constrained black-box optimization. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 1--8.

4. Algorithm selection based on exploratory landscape analysis and cost-sensitive learning

5. Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites

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