Diagnostic benchmarking of many-objective evolutionary algorithms for real-world problems-Reference-Cited by-同舟云学术

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Diagnostic benchmarking of many-objective evolutionary algorithms for real-world problems

Published:2024-08-22 Issue: Volume: Page:1-22
ISSN:0305-215X
Container-title:Engineering Optimization
language:en
Short-container-title:Engineering Optimization

Author:

Zatarain Salazar Jazmin¹²,Hadka David³,Reed Patrick²,Seada Haitham⁴,Deb Kalyanmoy⁴

Affiliation:

1. Faculty of Technology Policy and Management, Delft University of Technology, Delft, The Netherlands

2. Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA

3. Microsoft, Charlotte, NC, USA

4. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

Funder

US National Science Foundation (NSF)'s

Innovations at the Nexus of Food, Energy and Water Systems

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/0305215X.2024.2381818

Reference61 articles.

1. Hyper-heuristics: a survey of the state of the art

2. MANAGEMENT OF EUTROPHICATION FOR LAKES SUBJECT TO POTENTIALLY IRREVERSIBLE CHANGE

3. A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

4. Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

5. A Benchmark Test Suite for Evolutionary Many-Objective Optimization;Cheng Ran;Complex & Intelligent Systems,2017

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