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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Automated Algorithm Selection: Survey and Perspectives
Pascal Kerschke, Holger H. Hoos, Frank Neumann, et al.
Evolutionary Computation (2018) Vol. 27, Iss. 1, pp. 3-45
Open Access | Times Cited: 337

Showing 1-25 of 337 citing articles:

Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art
Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Patrick Meyer, et al.
European Journal of Operational Research (2021) Vol. 296, Iss. 2, pp. 393-422
Open Access | Times Cited: 292
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems
Eneko Osaba, Esther Villar-Rodríguez, Javier Del Ser, et al.
Swarm and Evolutionary Computation (2021) Vol. 64, pp. 100888-100888
Closed Access | Times Cited: 265
A Survey of Advances in Landscape Analysis for Optimisation
Katherine M. Malan
Algorithms (2021) Vol. 14, Iss. 2, pp. 40-40
Open Access | Times Cited: 104
Deep Reinforcement Learning Based Adaptive Operator Selection for Evolutionary Multi-Objective Optimization
Ye Tian, Xiaopeng Li, Haiping Ma, et al.
IEEE Transactions on Emerging Topics in Computational Intelligence (2022) Vol. 7, Iss. 4, pp. 1051-1064
Closed Access | Times Cited: 90
Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection
Fei Ming, Wenyin Gong, Ling Wang, et al.
IEEE/CAA Journal of Automatica Sinica (2024) Vol. 11, Iss. 4, pp. 919-931
Open Access | Times Cited: 18
A review on the self and dual interactions between machine learning and optimisation
Heda Song, Isaac Triguero, Ender Özcan
Progress in Artificial Intelligence (2019) Vol. 8, Iss. 2, pp. 143-165
Open Access | Times Cited: 83
A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection
Irfan Khan, Xianchao Zhang, Mobashar Rehman, et al.
IEEE Access (2020) Vol. 8, pp. 10262-10281
Open Access | Times Cited: 83
A practical tutorial on solving optimization problems via PlatEMO
Ye Tian, Weijian Zhu, Xingyi Zhang, et al.
Neurocomputing (2022) Vol. 518, pp. 190-205
Closed Access | Times Cited: 49
A survey of fitness landscape analysis for optimization
Feng Zou, Debao Chen, Hui Liu, et al.
Neurocomputing (2022) Vol. 503, pp. 129-139
Closed Access | Times Cited: 44
An algorithm selection approach for the flexible job shop scheduling problem: Choosing constraint programming solvers through machine learning
David Müller, Marcus Müller, Dominik Kreß, et al.
European Journal of Operational Research (2022) Vol. 302, Iss. 3, pp. 874-891
Closed Access | Times Cited: 41
Evolutionary Algorithms for Parameter Optimization—Thirty Years Later
Thomas Bäck, Anna V. Kononova, Bas van Stein, et al.
Evolutionary Computation (2023) Vol. 31, Iss. 2, pp. 81-122
Open Access | Times Cited: 37
Meta-Black-Box optimization for evolutionary algorithms: Review and perspective
Xu Yang, Rui Wang, Kaiwen Li, et al.
Swarm and Evolutionary Computation (2025) Vol. 93, pp. 101838-101838
Closed Access | Times Cited: 1
Accelerating process control and optimization via machine learning
Ilias Mitrai, Pródromos Daoutidis
Reviews in Chemical Engineering (2025)
Closed Access | Times Cited: 1
Auto-Sklearn 2.0: The Next Generation
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 70
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz–Beielstein, Carola Doerr, Jakob Bossek, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 68
Landscape-Aware Performance Prediction for Evolutionary Multiobjective Optimization
Arnaud Liefooghe, Fabio Daolio, Sebástien Vérel, et al.
IEEE Transactions on Evolutionary Computation (2019) Vol. 24, Iss. 6, pp. 1063-1077
Open Access | Times Cited: 67
A Recommender System for Metaheuristic Algorithms for Continuous Optimization Based on Deep Recurrent Neural Networks
Ye Tian, Shichen Peng, Xingyi Zhang, et al.
IEEE Transactions on Artificial Intelligence (2020) Vol. 1, Iss. 1, pp. 5-18
Closed Access | Times Cited: 65
Benchmarking discrete optimization heuristics with IOHprofiler
Carola Doerr, Furong Ye, Naama Horesh, et al.
Applied Soft Computing (2019) Vol. 88, pp. 106027-106027
Open Access | Times Cited: 55
Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
Quentin Renau, Carola Doerr, Johann Dréo, et al.
Lecture notes in computer science (2020), pp. 139-153
Open Access | Times Cited: 52
Per-run Algorithm Selection with Warm-Starting Using Trajectory-Based Features
Ana Kostovska, Anja Jankovič, Diederick Vermetten, et al.
Lecture notes in computer science (2022), pp. 46-60
Open Access | Times Cited: 30
Algorithm selection for protein–ligand docking: strategies and analysis on ACE
Tianlai Chen, Xiwen Shu, Huiyuan Zhou, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 20
Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python
Raphael Patrick Prager, Heike Trautmann
Evolutionary Computation (2023) Vol. 32, Iss. 3, pp. 211-216
Closed Access | Times Cited: 17
Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics
Diederick Vermetten, Carola Doerr, Hao Wang, et al.
Proceedings of the Genetic and Evolutionary Computation Conference (2024), pp. 41-49
Open Access | Times Cited: 8
Learn to optimize—a brief overview
Ke Tang, Xin Yao
National Science Review (2024) Vol. 11, Iss. 8
Open Access | Times Cited: 8
Predicting Good Configurations for GitHub and Stack Overflow Topic Models
Christoph Treude, Markus Wagner
(2019), pp. 84-95
Open Access | Times Cited: 45
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