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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:

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: 82

Showing 1-25 of 82 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: 281
Optimization problems for machine learning: A survey
Claudio Gambella, Bissan Ghaddar, Joe Naoum‐Sawaya
European Journal of Operational Research (2020) Vol. 290, Iss. 3, pp. 807-828
Open Access | Times Cited: 242
Machine learning to predict end stage kidney disease in chronic kidney disease
Qiong Bai, Chunyan Su, Wen Tang, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 88
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
Bo Peng, Jiahai Wang, Zizhen Zhang
Communications in computer and information science (2020), pp. 636-650
Open Access | Times Cited: 71
Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem
Maryam Karimi-Mamaghan, Mehrdad Mohammadi, Bastien Pasdeloup, et al.
European Journal of Operational Research (2022) Vol. 304, Iss. 3, pp. 1296-1330
Open Access | Times Cited: 67
An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem
Bingjie Li, Guohua Wu, Yong‐Ming He, et al.
IEEE/CAA Journal of Automatica Sinica (2022) Vol. 9, Iss. 7, pp. 1115-1138
Open Access | Times Cited: 64
Addressing the class-imbalance and class-overlap problems by a metaheuristic-based under-sampling approach
Paria Soltanzadeh, Mohammad‐Reza Feizi‐Derakhshi, Mahdi Hashemzadeh
Pattern Recognition (2023) Vol. 143, pp. 109721-109721
Closed Access | Times Cited: 27
The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement
Eghbal Hosseini, Abbas M. Al-Ghaili, Dler Hussein Kadir, et al.
IEEE Access (2024) Vol. 12, pp. 127440-127459
Open Access | Times Cited: 7
Software engineering challenges for machine learning applications: A literature review
Fumihiro Kumeno
Intelligent Decision Technologies (2020) Vol. 13, Iss. 4, pp. 463-476
Open Access | Times Cited: 66
Deep Learning for Proactive Network Monitoring and Security Protection
Giang Nguyen, Štefan Dlugolinský, Viet Tran, et al.
IEEE Access (2020) Vol. 8, pp. 19696-19716
Open Access | Times Cited: 59
General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance
Isaac Triguero, Daniel Molina, Javier Poyatos, et al.
Information Fusion (2023) Vol. 103, pp. 102135-102135
Open Access | Times Cited: 21
Nondominated sorting genetic algorithm-II with Q-learning for the distributed permutation flowshop rescheduling problem
Xin-Rui Tao, Quan-Ke Pan, Hongyan Sang, et al.
Knowledge-Based Systems (2023) Vol. 278, pp. 110880-110880
Closed Access | Times Cited: 18
NeuroCrossover: An intelligent genetic locus selection scheme for genetic algorithm using reinforcement learning
Haoqiang Liu, Zefang Zong, Yong Li, et al.
Applied Soft Computing (2023) Vol. 146, pp. 110680-110680
Closed Access | Times Cited: 16
Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19
Soufiane Hamida, Oussama El Gannour, Bouchaib Cherradi, et al.
(2020), pp. 1-6
Closed Access | Times Cited: 42
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations
Daniel Molina, Javier Poyatos, Javier Del Ser, et al.
Cognitive Computation (2020) Vol. 12, Iss. 5, pp. 897-939
Open Access | Times Cited: 41
A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems
Kayvan Asghari, Mohammad Masdari, Farhad Soleimanian Gharehchopogh, et al.
Progress in Artificial Intelligence (2021) Vol. 10, Iss. 3, pp. 349-374
Closed Access | Times Cited: 36
Choosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning
Felicia Schweitzer, Günter Bitsch, Louis Louw
Applied Sciences (2023) Vol. 13, Iss. 2, pp. 806-806
Open Access | Times Cited: 12
Design
Peter Childs, Marc A. Masen
Elsevier eBooks (2025), pp. 1-59
Closed Access
Leveraging machine learning and optimization models for enhanced seaport efficiency
Mahdi Jahangard, Ying Xie, Yuanjun Feng
Maritime Economics & Logistics (2025)
Open Access
Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications
José Barrera-García, Felipe Cisternas-Caneo, Broderick Crawford, et al.
Biomimetics (2023) Vol. 9, Iss. 1, pp. 9-9
Open Access | Times Cited: 11
Intelligent cross-entropy optimizer: A novel machine learning-based meta-heuristic for global optimization
Salar Farahmand‐Tabar, Payam Ashtari
Swarm and Evolutionary Computation (2024) Vol. 91, pp. 101739-101739
Closed Access | Times Cited: 3
EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification
Hoang Lam Le, Dario Landa-Silva, Mikel Galar, et al.
Applied Soft Computing (2020) Vol. 101, pp. 107033-107033
Open Access | Times Cited: 27
Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics
Broderick Crawford, Ricardo Soto, José Lemus-Romani, et al.
Mathematics (2021) Vol. 9, Iss. 16, pp. 1839-1839
Open Access | Times Cited: 25
ML meets MLn: Machine learning in ligand promoted homogeneous catalysis
Jonathan D. Hirst, Samuel Boobier, Jennifer Coughlan, et al.
Artificial Intelligence Chemistry (2023) Vol. 1, Iss. 2, pp. 100006-100006
Open Access | Times Cited: 10
Designing New Metaheuristics: Manual Versus Automatic Approaches
Christian Leonardo Camacho-Villalón, Thomas Stützle, Marco Dorigo
Intelligent Computing (2023) Vol. 2
Open Access | Times Cited: 10
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