Anytime Performance Assessment in Blackbox Optimization Benchmarking

Author:

Hansen Nikolaus1,Auger Anne1,Brockhoff Dimo1ORCID,Tusar Tea2ORCID

Affiliation:

1. Inria and Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France

2. Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia

Funder

French National Research Agency

Slovenian Research Agency

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Theoretical Computer Science,Software

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14

2. GRAHF: A Hyper-Heuristic Framework for Evolving Heterogeneous Island Model Topologies;Proceedings of the Genetic and Evolutionary Computation Conference;2024-07-14

3. Enhancing Algorithm Performance Prediction in Constrained Multiobjective Optimization Using Additional Training Problems;Proceedings of the Genetic and Evolutionary Computation Conference;2024-07-14

4. Predicting Algorithm Performance in Constrained Multiobjective Optimization: A Tough Nut to Crack;Lecture Notes in Computer Science;2024

5. AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign;2023 International Seminar on Application for Technology of Information and Communication (iSemantic);2023-09-16

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