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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:

Explainable Landscape-Aware Optimization Performance Prediction
Risto Trajanov, Stefan Dimeski, Martin Popovski, et al.
2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2021), pp. 01-08
Open Access | Times Cited: 20

Showing 20 citing articles:

Explainable Benchmarking for Iterative Optimization Heuristics
Bas van Stein, Diederick Vermetten, Anna V. Kononova, et al.
ACM Transactions on Evolutionary Learning and Optimization (2025)
Open Access
Benchmarking footprints of continuous black-box optimization algorithms: Explainable insights into algorithm success and failure
Ana Nikolikj, Mario Andrés Muñoz, Tome Eftimov
Swarm and Evolutionary Computation (2025) Vol. 94, pp. 101895-101895
Open Access
Adaptive local landscape feature vector for problem classification and algorithm selection
Yaxin Li, Jing Liang, Kunjie Yu, et al.
Applied Soft Computing (2022) Vol. 131, pp. 109751-109751
Closed Access | Times Cited: 12
Using Affine Combinations of BBOB Problems for Performance Assessment
Diederick Vermetten, Furong Ye, Carola Doerr
Proceedings of the Genetic and Evolutionary Computation Conference (2023), pp. 873-881
Open Access | Times Cited: 6
Explainable Landscape Analysis in Automated Algorithm Performance Prediction
Risto Trajanov, Stefan Dimeski, Martin Popovski, et al.
Lecture notes in computer science (2022), pp. 207-222
Closed Access | Times Cited: 10
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances
Ana Nikolikj, Sašo Džeroski, Mario Andrés Muñoz, et al.
Proceedings of the Genetic and Evolutionary Computation Conference (2023), pp. 529-537
Open Access | Times Cited: 4
A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis
Ningning Zhu, Fuqing Zhao, Yang Yu, et al.
Swarm and Evolutionary Computation (2024) Vol. 90, pp. 101669-101669
Closed Access | Times Cited: 1
Properties of objective functions and search algorithms in multi-objective optimization problems
A. V. Smirnov
Russian Technological Journal (2022) Vol. 10, Iss. 4, pp. 75-85
Open Access | Times Cited: 4
From Fitness Landscapes to Explainable AI and Back
Sarah L. Thomson, Jason Adair, Alexander E. I. Brownlee, et al.
(2023), pp. 1663-1667
Open Access | Times Cited: 2
Comparing Solvability Patterns of Algorithms across Diverse Problem Landscapes
Ana Nikolikj, Tome Eftimov
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2024), pp. 143-146
Closed Access
Explaining Differential Evolution Performance Through Problem Landscape Characteristics
Ana Nikolikj, Ryan Dieter Lang, Peter Korošec, et al.
Lecture notes in computer science (2022), pp. 99-113
Closed Access | Times Cited: 3
Using LLM for Automatic Evolvement of Metaheuristics from Swarm Algorithm SOMA
Michal Pluháček, Jozef Kováč, Adam Viktorin, et al.
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2024), pp. 2018-2022
Closed Access
Explaining evolutionary feature selection via local optima networks
Jason Adair, Sarah L. Thomson, Alexander E. I. Brownlee
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2024), pp. 1573-1581
Open Access
Identifying minimal set of Exploratory Landscape Analysis features for reliable algorithm performance prediction
Ana Nikolikj, Risto Trajanov, Gjorgjina Cenikj, et al.
2022 IEEE Congress on Evolutionary Computation (CEC) (2022), pp. 1-8
Closed Access | Times Cited: 2
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances
Ana Nikolikj, Sašo Džeroski, Mario Andrés Muñoz, et al.
arXiv (Cornell University) (2023)
Open Access
A Dynamic Hierarchical Hyperheuristic Based Automatic Algorithm Design with Reinforcement Learning
Ningning Zhu, Fuqing Zhao, Jie Cao, et al.
(2023)
Closed Access
Investigation of influence of objective function valley ratio on the determination error of its minimum coordinates
A. V. Smirnov
Russian Technological Journal (2023) Vol. 11, Iss. 6, pp. 57-67
Open Access
A Hierarchical Reinforcement Learning-Aware Hyper-Heuristic Algorithm with Fitness Landscape Analysis
Ningning Zhu, Fuqing Zhao, Yang Yu, et al.
(2023)
Closed Access
Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization
Urban Škvorc, Tome Eftimov, Peter Koroec
2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2023), pp. 335-340
Closed Access
Comparison of algorithms for multi-objective optimization of radio technical device characteristics
A. V. Smirnov
Russian Technological Journal (2022) Vol. 10, Iss. 6, pp. 42-51
Open Access
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