A review on the self and dual interactions between machine learning and optimisation

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works

Convex Optimization

Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601 · see citing works

Adaptation in Natural and Artificial Systems

Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340 · see citing works

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works

Data Mining: Practical Machine Learning Tools and Techniques

Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

Particle swarm optimization: An overview

Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101 · see citing works

Neural networks and physical systems with emergent collective computational abilities.

Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270 · see citing works

Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,614 · see citing works

Finding groups in data: an introduction to cluster analysis

(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,537 · see citing works

Learning from Imbalanced Data

He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works

Pattern Recognition and Machine Learning

(2007) · Kybernetes · doi:10.1108/03684920710743466 · Cited by 8,540 · see citing works

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512 · see citing works

Ant colony system: a cooperative learning approach to the traveling salesman problem

Dorigo, M., & Gambardella, L. M. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585892 · Cited by 8,081 · see citing works

Untitled work

(2012) · Cited by 8,009 · see citing works

Proceedings of the 25th international conference on Machine learning - ICML '08

(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works

Practical Bayesian Optimization of Machine Learning Algorithms

Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691 · see citing works

Prototypical Networks for Few-shot LearningPreprint

Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 5,863 · see citing works

Tabu Search—Part II

Glover, F. (1990) · INFORMS journal on computing · doi:10.1287/ijoc.2.1.4 · Cited by 5,676 · see citing works

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319 · see citing works

Tabu Search—Part I

Glover, F. (1989) · INFORMS journal on computing · doi:10.1287/ijoc.1.3.190 · Cited by 5,122 · see citing works

An overview of gradient descent optimization algorithms

Ruder, S. (2016) · arXiv · doi:10.48550/arxiv.1609.04747 · Cited by 4,962 · see citing works

Matching Networks for One Shot LearningPreprint

Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04080 · Cited by 4,874 · see citing works

Convolutional networks for images, speech, and time series

LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,637 · see citing works

Neural network ensembles

Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260 · see citing works

Siamese Neural Networks for One-shot Image Recognition

Koch, G. E., Zemel, R. S., & Salakhutdinov, R. (2015) · Cited by 3,712 · see citing works

One-shot learning of object categories

Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.79 · Cited by 3,029 · see citing works

Evolving artificial neural networks

Yao, X. (1999) · Proceedings of the IEEE · doi:10.1109/5.784219 · Cited by 2,995 · see citing works

Apprenticeship learning via inverse reinforcement learning

Abbeel, P., & Ng, A. Y. (2004) · doi:10.1145/1015330.1015430 · Cited by 2,873 · see citing works

Optimization as a Model for Few-Shot Learning

Ravi, S., & Larochelle, H. (2017) · International Conference on Learning Representations · Cited by 2,766 · see citing works

Untitled work

(1998) · Cited by 2,679 · see citing works

Data mining with big data

Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.109 · Cited by 2,410 · see citing works

On the momentum term in gradient descent learning algorithms

Ning Qian, A. (1999) · Neural Networks · doi:10.1016/s0893-6080(98)00116-6 · Cited by 2,318 · see citing works

Stanley: The robot that won the DARPA Grand Challenge

Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., et al. (2006) · Journal of Field Robotics · doi:10.1002/rob.20147 · Cited by 2,237 · see citing works

The KDD process for extracting useful knowledge from volumes of data

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996) · Communications of the ACM · doi:10.1145/240455.240464 · Cited by 1,934 · see citing works

A Survey on Evolutionary Computation Approaches to Feature Selection

Xue, B., Zhang, M., Browne, W. N., & Yao, X. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2504420 · Cited by 1,853 · see citing works

Genetic programming as a means for programming computers by natural selection

Koza, J. (1994) · Statistics and Computing · doi:10.1007/bf00175355 · Cited by 1,685 · see citing works

Automated Machine Learning

Hutter, F., Kotthoff, L., & Vanschoren, J. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5 · Cited by 1,552 · see citing works

Surrogate-assisted evolutionary computation: Recent advances and future challenges

Jin, Y. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.05.001 · Cited by 1,547 · see citing works

Meta-learning with memory-augmented neural networks

Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016) · International Conference on Machine Learning · Cited by 1,504 · see citing works

Learning to learn by gradient descent by gradient descent

Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., & de Freitas, N. (2016) · arXiv · doi:10.48550/arxiv.1606.04474 · Cited by 1,486 · see citing works

On First-Order Meta-Learning Algorithms

Nichol, A., Achiam, J., & Schulman, J. (2018) · arXiv · doi:10.48550/arxiv.1803.02999 · Cited by 1,438 · see citing works

Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms

Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487629 · Cited by 1,359 · see citing works

Untitled work

(2015) · neural information processing systems · Cited by 1,305 · see citing works

Learning to Learn

Thrun, S., & Pratt, L. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5529-2 · Cited by 1,291 · see citing works

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

Burke, E. K., Michel Gendreau, A., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., & Qu, R. (2013) · Journal of the Operational Research Society · doi:10.1057/jors.2013.71 · Cited by 1,272 · see citing works

The Algorithm Selection Problem

Rice, J. R. (1976) · Advances in computers · doi:10.1016/s0065-2458(08)60520-3 · Cited by 1,210 · see citing works

Iterated Local Search

Ramalhinho, H., Martin, O., & Stützle, T. (2006) · Kluwer Academic Publishers eBooks · doi:10.1007/0-306-48056-5_11 · Cited by 1,172 · see citing works

A perspective view and survey of meta-learning

Vilalta, R., & Drissi, Y. (2002) · Artificial Intelligence Review · doi:10.1023/a:1019956318069 · Cited by 1,108 · see citing works

Space Mapping: The State of the Art

Bandler, J. W., Cheng, Q. S., Dakroury, S. A., Mohamed, A. S. A., Bakr, M. H., Madsen, K. H., & Søndergaard, J. (2004) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2003.820904 · Cited by 1,067 · see citing works

A Simple Neural Attentive Meta-LearnerPreprint

Mishra, N., Rohaninejad, M., Chen, X., & Abbeel, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.03141 · Cited by 961 · see citing works

Optimization for Machine Learning

Sra, S., Nowozin, S., & Wright, S. J. (2011) · The MIT Press eBooks · doi:10.7551/mitpress/8996.001.0001 · Cited by 909 · see citing works

Few-Shot Learning with Graph Neural NetworksPreprint

García, V., & Bruna, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04043 · Cited by 863 · see citing works

A Survey of Evolutionary Algorithms for Clustering

Hruschka, E. R., Campello, R. J. G. B., Freitas, A. A., & de Carvalho, A. C. P. L. F. (2009) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2008.2007252 · Cited by 761 · see citing works

Meta NetworksPreprint

Munkhdalai, T., & Yu, H. Q. (2017) · arXiv (Cornell University) · Cited by 694 · see citing works

Memetic algorithms and memetic computing optimization: A literature review

Neri, F., & Cotta, C. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.11.003 · Cited by 687 · see citing works

Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]

Ren, Y., Zhang, L., & Suganthan, P. N. (2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2471235 · Cited by 681 · see citing works

Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA

Kotthoff, L., Thornton, C., Hoos, H. H., Hutter, F., & Leyton‐Brown, K. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5_4 · Cited by 671 · see citing works

A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

Zhang, X., Tian, Y., Cheng, R., & Jin, Y. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2600642 · Cited by 666 · see citing works

Book Review: Pattern Recognition and Machine Learning

Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

Olson, R. S., Bartley, N., Urbanowicz, R. J., & Moore, J. H. (2016) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2908812.2908918 · Cited by 630 · see citing works

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha, V., Abraham, A., & Snåšel, V. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.01.013 · Cited by 613 · see citing works

A Classification of Hyper-heuristic Approaches

Burke, E. K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., & Woodward, J. R. (2010) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-1-4419-1665-5_15 · Cited by 590 · see citing works

Few-Shot Image Recognition by Predicting Parameters from Activations

Qiao, S., Liu, C., Shen, W., & Yuille, A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00755 · Cited by 565 · see citing works

A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning

García, S., Luengo, J., Sáez, J. A., López, V., & Herrera, F. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.35 · Cited by 552 · see citing works

Advances in Instance Selection for Instance-Based Learning Algorithms

Brighton, H., & Mellish, C. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1014043630878 · Cited by 534 · see citing works

Parameter Control in Evolutionary Algorithms: Trends and Challenges

Karafotias, G., Hoogendoorn, M., & Eiben, A. E. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2308294 · Cited by 523 · see citing works

An ant colony approach for clustering

Shelokar, P., Jayaraman, V., & Kulkarni, B. D. (2004) · Analytica Chimica Acta · doi:10.1016/j.aca.2003.12.032 · Cited by 516 · see citing works

A Tabu-Search Hyperheuristic for Timetabling and Rostering

Burke, E. K., Kendall, G., & Soubeiga, E. (2003) · Journal of Heuristics · doi:10.1023/b:heur.0000012446.94732.b6 · Cited by 509 · see citing works

A graph-based hyper-heuristic for educational timetabling problems

Burke, E. K., McCollum, B., Meisels, A., Petrović, S., & Qu, R. (2005) · European Journal of Operational Research · doi:10.1016/j.ejor.2005.08.012 · Cited by 507 · see citing works

Evolutionary tuning of multiple SVM parameters

Friedrichs, F., & Igel, C. (2005) · Neurocomputing · doi:10.1016/j.neucom.2004.11.022 · Cited by 500 · see citing works

SMASH: One-Shot Model Architecture Search through HyperNetworksPreprint

Brock, A., Lim, T., Ritchie, J. M., & Weston, N. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.05344 · Cited by 500 · see citing works

A Hyperheuristic Approach to Scheduling a Sales Summit

Cowling, P., Kendall, G., & Soubeiga, E. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44629-x_11 · Cited by 476 · see citing works

Automated Design of Production Scheduling Heuristics: A Review

Branke, J., Nguyen, S., Pickardt, C. W., & Zhang, M. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2429314 · Cited by 474 · see citing works

Learning to Model the Tail

Wang, Y.-X., Ramanan, D., & Hebert, M. (2017) · neural information processing systems · Cited by 468 · see citing works

Automated Algorithm Selection: Survey and Perspectives

Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2018) · Evolutionary Computation · doi:10.1162/evco_a_00242 · Cited by 440 · see citing works

Bayesian Approach to Global Optimization--Theory and Applications.

Boender, C. G. E., & Mockus, J. (1991) · Mathematics of Computation · doi:10.2307/2008419 · Cited by 425 · see citing works

Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy

García, S., & Herrera, F. (2009) · Evolutionary Computation · doi:10.1162/evco.2009.17.3.275 · Cited by 423 · see citing works

A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I

Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Coello, C. A. C. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2290086 · Cited by 423 · see citing works

Clustering and Classification.

Arabie, P., Hubert, L. J., & Soete, G. D. (1997) · Biometrics · doi:10.2307/2533584 · Cited by 414 · see citing works

Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results

Brazdil, P., Soares, C., & da Costa, J. P. (2003) · Machine Learning · doi:10.1023/a:1021713901879 · Cited by 409 · see citing works

Learning feed-forward one-shot learnersPreprint

Bertinetto, L., Henriques, J. F., Valmadre, J., Torr, P. H. S., & Vedaldi, A. (2016) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1606.05233 · Cited by 393 · see citing works

A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem

Mahi, M., Baykan, Ö. K., & Kodaz, H. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.01.068 · Cited by 380 · see citing works

Finding Groups in Data: An Introduction to Cluster Analysis

Hadi, A. S., Kaufman, L., & Rousseeuw, P. J. (1992) · Technometrics · doi:10.2307/1269576 · Cited by 380 · see citing works

Clustering and Classification

Arabie, P., Hubert, L. J., & Soete, G. D. (1996) · WORLD SCIENTIFIC eBooks · doi:10.1142/1930 · Cited by 369 · see citing works

Metalearning: A survey of trends and technologies

Lemke, C., Budka, M., & Gabrys, B. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-013-9406-y · Cited by 365 · see citing works

Non-convex Optimization for Machine Learning

Jain, P., & Kar, P. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000058 · Cited by 364 · see citing works

Meta-Learning

Vanschoren, J. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5_2 · Cited by 361 · see citing works

Stereotyping: improving particle swarm performance with cluster analysis

Kennedy, J. (2002) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2000.870832 · Cited by 354 · see citing works

A Survey of Evolutionary Algorithms for Decision-Tree Induction

Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. P. L. F., & Freitas, A. A. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2157494 · Cited by 347 · see citing works

Exploring Hyper-heuristic Methodologies with Genetic Programming

Burke, E. K., Hyde, M. R., Kendall, G., Ochoa, G., Özcan, E., & Woodward, J. R. (2009) · Intelligent systems reference library · doi:10.1007/978-3-642-01799-5_6 · Cited by 310 · see citing works

Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

Zhang, J., Chung, H. S.-H., & Lo, A. W.-L. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2006.880727 · Cited by 307 · see citing works

Objective Reduction in Many-Objective Optimization: Linear and Nonlinear Algorithms

Saxena, D. K., Duro, J. A., Tiwari, A., Deb, K., & Zhang, Q. (2012) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2012.2185847 · Cited by 303 · see citing works

Evolutionary Computation Meets Machine Learning: A Survey

Zhang, J., Zhan, Z., Lin, Y., Chen, N., Gong, Y., Zhong, J., Chung, H. S.-H., Li, Y., & Shi, Y. (2011) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2011.942584 · Cited by 302 · see citing works

The Bayesian approach to global optimization

Mockus, A. (2005) · Springer eBooks · doi:10.1007/bfb0006170 · Cited by 290 · see citing works

Genetic programming for production scheduling: a survey with a unified framework

Nguyen, S., Mei, Y., & Zhang, M. (2017) · Complex & Intelligent Systems · doi:10.1007/s40747-017-0036-x · Cited by 281 · see citing works

Metalearning

Brazdil, P., Giraud-Carrier, C. G., Soares, C., & Vilalta, R. (2008) · Cognitive technologies · doi:10.1007/978-3-540-73263-1 · Cited by 265 · see citing works

Taking Human out of Learning Applications: A Survey on Automated Machine LearningPreprint

Yao, Q., Wang, M., Escalante, H. J., Guyon, I., Hu, Y.-Q., Li, Y., Tu, W.-W., Yang, Q., & Yang, Y. (2018) · arXiv (Cornell University) · Cited by 261 · see citing works

An ant colony algorithm for the multi-compartment vehicle routing problem

Reed, M. B., Yiannakou, A., & Evering, R. (2013) · Applied Soft Computing · doi:10.1016/j.asoc.2013.10.017 · Cited by 245 · see citing works

Meta Learning Shared HierarchiesPreprint

Frans, K., Ho, J., Chen, X., Abbeel, P., & Schulman, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.09767 · Cited by 219 · see citing works

An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

Deng, Y., Liu, Y., & Zhou, D. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/212794 · Cited by 214 · see citing works

Opt4J

Lukasiewycz, M., Glaß, M., Reimann, F., & Teich, J. (2011) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2001576.2001808 · Cited by 204 · see citing works

MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules

Alataş, B., Akın, E., & Karcı, A. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2007.05.003 · Cited by 201 · see citing works

Towards objective measures of algorithm performance across instance space

Smith‐Miles, K., Baatar, D., Wreford, B., & Lewis, R. (2013) · Computers & Operations Research · doi:10.1016/j.cor.2013.11.015 · Cited by 194 · see citing works

Non-convex Optimization for Machine Learning

Jain, P., & Kar, P. (2017) · now publishers, Inc. eBooks · doi:10.1561/9781680833690 · Cited by 191 · see citing works

A Classification of Hyper-Heuristic Approaches: Revisited

Burke, E. K., Hyde, M. R., Kendall, G., Ochoa, G., Özcan, E., & Woodward, J. R. (2018) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-319-91086-4_14 · Cited by 190 · see citing works

Meta-Learning: A SurveyPreprint

Vanschoren, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03548 · Cited by 187 · see citing works

Ensemble Learning Using Multi-Objective Evolutionary Algorithms

Chandra, A., & Yao, X. (2006) · Journal of Mathematical Modelling and Algorithms · doi:10.1007/s10852-005-9020-3 · Cited by 179 · see citing works

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Calvet, L., de Armas, J., Masip, D., & Juan, A. A. (2017) · Open Mathematics · doi:10.1515/math-2017-0029 · Cited by 177 · see citing works

Accelerating Neural Architecture Search using Performance PredictionPreprint

Baker, B., Gupta, O., Raskar, R., & Naik, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10823 · Cited by 174 · see citing works

A Survey of Multiobjective Evolutionary Clustering

Mukhopadhyay, A., Maulik, U., & Bandyopadhyay, S. (2015) · ACM Computing Surveys · doi:10.1145/2742642 · Cited by 168 · see citing works

Learning Algorithms for Active LearningPreprint

Bachman, P., Sordoni, A., & Trischler, A. (2017) · International Conference on Machine Learning · doi:10.48550/arxiv.1708.00088 · Cited by 166 · see citing works

Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II

Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Coello, C. A. C. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2290082 · Cited by 164 · see citing works

Metaheuristics

Sörensen, K., & Glover, F. (2013) · Springer eBooks · doi:10.1007/978-1-4419-1153-7_1167 · Cited by 156 · see citing works

Choosing Search Heuristics by Non-Stationary Reinforcement Learning

Nareyek, A. (2003) · Applied optimization · doi:10.1007/978-1-4757-4137-7_25 · Cited by 146 · see citing works

Objective reduction using a feature selection technique

Jáimes, A. L., Coello, C. A. C., & Chakraborty, D. (2008) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1389095.1389228 · Cited by 145 · see citing works

The Interplay of Optimization and Machine Learning Research

Bennett, K. P., & Parrado-Hernández, E. (2006) · doi:10.5555/1248547.1248593 · Cited by 141 · see citing works

Automatic design of hyper-heuristic based on reinforcement learning

Choong, S. S., Wong, L., & Lim, C. P. (2018) · Information Sciences · doi:10.1016/j.ins.2018.01.005 · Cited by 139 · see citing works

An efficient genetic algorithm with less fitness evaluation by clustering

Kim, H.-S., & Cho, S. (2002) · Congress on Evolutionary Computation · doi:10.1109/cec.2001.934284 · Cited by 136 · see citing works

Design of the 2015 ChaLearn AutoML challenge

Guyon, I., Bennett, K. P., Cawley, G. C., Escalante, H. J., Escalera, S., Ho, T. K., Macià, N., Ray, B., Saeed, M., Statnikov, A., et al. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280767 · Cited by 129 · see citing works

Characterization of classification algorithms

da Gama, J. M. P., & Brazdil, P. (1995) · Lecture notes in computer science · doi:10.1007/3-540-60428-6_16 · Cited by 123 · see citing works

Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification

Isaac Triguero, A., García, S., & Herrera, F. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.10.020 · Cited by 123 · see citing works

Local Item-Item Models For Top-N Recommendation

Christakopoulou, E., & Karypis, G. (2016) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2959100.2959185 · Cited by 120 · see citing works

K-harmonic means data clustering with simulated annealing heuristic

Güngör, Z., & Ünler, A. (2006) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.05.166 · Cited by 113 · see citing works

An Enhanced tabu search algorithm to minimize a bi-criteria objective in batching and scheduling problems on unrelated-parallel machines with desired lower bounds on batch sizes

Shahvari, O., & Logendran, R. (2016) · Computers & Operations Research · doi:10.1016/j.cor.2016.07.021 · Cited by 107 · see citing works

Hybridization of evolutionary algorithms and local search by means of a clustering method

Martínez-Estudillo, A. C., Hervás‐Martínez, C., Martínez-Estudillo, F. J., & García‐Pedrajas, N. (2006) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2005.860138 · Cited by 104 · see citing works

Prodock: Software package for protein modeling and docking

Trosset, J., & Scheraga, H. A. (1999) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199903)20:4 · Cited by 103 · see citing works

Comparison of metamodeling techniques in evolutionary algorithms

Diaz-Manríquez, A., Toscano‐Pulido, G., & Coello, C. A. C. (2016) · Soft Computing · doi:10.1007/s00500-016-2140-z · Cited by 102 · see citing works

HeuristicLab: A Generic and Extensible Optimization Environment

Wagner, Ş., & Affenzeller, M. (2005) · Springer eBooks · doi:10.1007/3-211-27389-1_130 · Cited by 99 · see citing works

A Taxonomy for Metamodeling Frameworks for Evolutionary Multiobjective Optimization

Deb, K., Hussein, R., Roy, P. C., & Toscano‐Pulido, G. (2018) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2018.2828091 · Cited by 98 · see citing works

Advances in data stream mining

Gaber, M. M. (2011) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.52 · Cited by 92 · see citing works

A Review of Recent Research in Metareasoning and Metalearning

Anderson, M. L., & Oates, T. (2007) · doi:10.1609/aimag.v28i1.2025 · Cited by 91 · see citing works

A Taxonomy of Cooperative Search Algorithms

El-Abd, M., & Kamel, M. S. (2005) · Lecture notes in computer science · doi:10.1007/11546245_4 · Cited by 89 · see citing works

Synergies between operations research and data mining: The emerging use of multi-objective approaches

Corne, D., Dhaenens, C., & Jourdan, L. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2012.03.039 · Cited by 80 · see citing works

A Review of evolutionary Algorithms for Data Mining

Freitas, A. A. (2007) · Springer eBooks · doi:10.1007/978-0-387-69935-6_4 · Cited by 80 · see citing works

Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets

Barros, R. C., Basgalupp, M. P., Freitas, A. A., & de Carvalho, A. C. P. L. F. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2291813 · Cited by 76 · see citing works

Empirical Learning as a Function of Concept Character

Rendell, L. A., & Cho, H. (1990) · Machine Learning · doi:10.1023/a:1022651406695 · Cited by 76 · see citing works

Iterated local search using an add and delete hyper-heuristic for university course timetabling

Soria-Alcaraz, J. A., Özcan, E., Swan, J., Kendall, G., & Carpio, M. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.11.043 · Cited by 67 · see citing works

Performance Analysis of Continuous Black-Box Optimization Algorithms via Footprints in Instance Space

Muñoz, M. A., & Smith‐Miles, K. (2016) · Evolutionary Computation · doi:10.1162/evco_a_00194 · Cited by 65 · see citing works

Using Datamining Techniques to Help Metaheuristics: A Short Survey

Jourdan, L., Dhaenens, C., & Talbi, E. (2006) · Lecture notes in computer science · doi:10.1007/11890584_5 · Cited by 64 · see citing works

Power economic dispatch using a hybrid genetic algorithm

Yalçinöz, T., & Altun, H. (2001) · IEEE Power Engineering Review · doi:10.1109/39.911360 · Cited by 63 · see citing works

A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention

Guyon, I., Chaabane, I., Escalante, H. J., Escalera, S., Jajetic, D., Lloyd, J. R., Macià, N., Ray, B., Romaszko, Ł., Sébag, M., et al. (2016) · HAL · Cited by 61 · see citing works

An Intelligent Hyper-Heuristic Framework for CHeSC 2011

Mısır, M., Verbeeck, K., Causmaecker, P. D., & den Berghe, G. V. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34413-8_45 · Cited by 60 · see citing works

Software module clustering using a hyper-heuristic based multi-objective genetic algorithm

Kumari, A. C., Srinivas, K., & Gupta, M. P. (2013) · IEEE International Advance Computing Conference · doi:10.1109/iadcc.2013.6514331 · Cited by 60 · see citing works

Markov chain hyper-heuristic (MCHH)

McClymont, K., & Keedwell, E. C. (2011) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2001576.2001845 · Cited by 60 · see citing works

How Meta-heuristic Algorithms Contribute to Deep Learning in the Hype of Big Data Analytics

Fong, S. J., Deb, S., & Yang, X. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-3373-5_1 · Cited by 60 · see citing works

An Improved Choice Function Heuristic Selection for Cross Domain Heuristic Search

Drake, J. H., Özcan, E., & Burke, E. K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32964-7_31 · Cited by 59 · see citing works

BEE COLONY OPTIMIZATION WITH LOCAL SEARCH FOR TRAVELING SALESMAN PROBLEM

Wong, L., Low, M. Y. H., & Chong, C. S. (2010) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213010000200 · Cited by 57 · see citing works

Speeding up algorithm selection using average ranking and active testing by introducing runtime

Abdulrahman, S. M., Brazdil, P., van Rijn, J. N., & Vanschoren, J. (2017) · Machine Learning · doi:10.1007/s10994-017-5687-8 · Cited by 52 · see citing works

Initializing K-Means Using Genetic Algorithms

Al-Shboul, B. A., & Myaeng, S.-H. (2009) · World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering · doi:10.5281/zenodo.1080069 · Cited by 50 · see citing works

Multivariate Discretization Based on Evolutionary Cut Points Selection for Classification

Ramírez‐Gallego, S., García, S., Benítez, J. M., & Herrera, F. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2410143 · Cited by 49 · see citing works

A tensor-based selection hyper-heuristic for cross-domain heuristic search

Asta, S., & Özcan, E. (2014) · Information Sciences · doi:10.1016/j.ins.2014.12.020 · Cited by 47 · see citing works

On the discovery of association rules by means of evolutionary algorithms

del Jesús, M. J., Gámez, J. A., González, P., & Puerta, J. M. (2011) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.18 · Cited by 46 · see citing works

Evolutionary design of Evolutionary Algorithms

Dioşan, L. S., & Oltean, M. (2009) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-009-9081-6 · Cited by 44 · see citing works

Learning heuristic selection using a Time Delay Neural Network for Open Vehicle Routing

Tyasnurita, R., Özcan, E., & John, R. I. (2017) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2017.7969477 · Cited by 43 · see citing works

A tensor based hyper-heuristic for nurse rostering

Asta, S., Özcan, E., & Curtois, T. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.01.031 · Cited by 40 · see citing works

A unified evolutionary training scheme for single and ensemble of feedforward neural network

Chen, W.-C., Tseng, L., & Wu, C.-S. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.05.057 · Cited by 39 · see citing works

Adaptive evolutionary programming based on reinforcement learning

ZHANG, H., & LU, J. (2007) · Information Sciences · doi:10.1016/j.ins.2007.09.026 · Cited by 39 · see citing works

Co-evolving Memetic Algorithms: Initial Investigations

Smith, J. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45712-7_52 · Cited by 38 · see citing works

Learning to Learn from Weak Supervision by Full SupervisionPreprint

Dehghani, M., Severyn, A., Rothe, S., & Kamps, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.11383 · Cited by 38 · see citing works

Multi-label classification from high-speed data streams with adaptive model rules and random rules

Sousa, R. T., & da Gama, J. M. P. (2018) · Progress in Artificial Intelligence · doi:10.1007/s13748-018-0142-z · Cited by 37 · see citing works

Meta-modeling using generalized regression neural network and particle swarm optimization

Park, J., & Kim, K.-Y. J. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.11.029 · Cited by 36 · see citing works

Surrogate-assisted multi-objective model selection for support vector machines

Rosales-Pérez, A., González, J. A., Coello, C. A. C., Escalante, H. J., & Reyes-Garcia, C. A. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.08.075 · Cited by 34 · see citing works

Ensemble learning for free with evolutionary algorithms?

Gagné, C., Sébag, M., Schoenauer, M., & Tomassini, M. (2007) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1276958.1277317 · Cited by 33 · see citing works

Evaluation of a Family of Reinforcement Learning Cross-Domain Optimization Heuristics

Gaspero, L. D., & Urli, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34413-8_32 · Cited by 29 · see citing works

A Clustering Based Niching Method for Evolutionary Algorithms

Streichert, F., Stein, G., Ulmer, H., & Zell, A. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45105-6_79 · Cited by 28 · see citing works

An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex

Asta, S., & Özcan, E. (2014) · IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) · doi:10.1109/eals.2014.7009505 · Cited by 27 · see citing works

Multiobjective Metaheuristic to Design RNA Sequences

Rubio‐Largo, Á., Vanneschi, L., Castelli, M., & Vega-Rodríguez, M. A. (2018) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2018.2844116 · Cited by 27 · see citing works

A self-adaptive Multimeme Memetic Algorithm co-evolving utility scores to control genetic operators and their parameter settings

Özcan, E., Drake, J. H., Altıntaş, C., & Asta, S. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.07.032 · Cited by 26 · see citing works

Integrating neural networks and logistic regression to underpin hyper-heuristic search

Li, J., Burke, E. K., & Qu, R. (2010) · Knowledge-Based Systems · doi:10.1016/j.knosys.2010.10.004 · Cited by 23 · see citing works

Evolutionary algorithms for real world applications [Application Notes]

Bäck, T., Emmerich, M., & Shir, O. M. (2008) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2007.913378 · Cited by 21 · see citing works

Generating Applicable Synthetic Instances for Branch Problems

Lopes, L., & Smith‐Miles, K. (2013) · Operations Research · doi:10.1287/opre.2013.1169 · Cited by 19 · see citing works

Limits to Learning in Reinforcement Learning Hyper-heuristics

Alanazi, F., & Lehre, P. K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30698-8_12 · Cited by 19 · see citing works

HHFS: Hyper-heuristic feature selection

Montazeri, M. (2016) · Intelligent Data Analysis · doi:10.3233/ida-160840 · Cited by 16 · see citing works

A pattern recognition based intelligent search method and two assignment problem case studies

Li, J., Burke, E. K., & Qu, R. (2010) · Applied Intelligence · doi:10.1007/s10489-010-0270-z · Cited by 15 · see citing works

A Generic Bee Colony Optimization Framework for Combinatorial Optimization Problems

Wong, L., Low, M. Y. H., & Chong, C. S. (2010) · Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation · doi:10.1109/ams.2010.41 · Cited by 13 · see citing works

A hyper-heuristic approach to design and tuning heuristic methods for web document clustering

Cobos, C., Mendoza, M., & León, E. (2011) · doi:10.1109/cec.2011.5949773 · Cited by 13 · see citing works

Introduction: Optimization and Machine Learning

Sra, S., Nowozin, S., & Wright, S. J. (2011) · The MIT Press eBooks · doi:10.7551/mitpress/8996.003.0003 · Cited by 13 · see citing works

A hyper-heuristic clustering algorithm

Tsai, C., Song, H.-J., & Chiang, M. (2012) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2012.6378179 · Cited by 11 · see citing works

A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems

Ortíz-Bayliss, J. C., Terashima‐Marín, H., & Conant-Pablos, S. E. (2015) · Cognitive Computation · doi:10.1007/s12559-015-9368-2 · Cited by 11 · see citing works

Offline Learning for Selection Hyper-heuristics with Elman Networks

Yates, W. B., & Keedwell, E. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78133-4_16 · Cited by 10 · see citing works

A Hyper-Heuristic Evolutionary Algorithm for Learning Bayesian Network Classifiers

de Sá, A. G. C., & Pappa, G. L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12027-0_35 · Cited by 8 · see citing works

Evolvability Metric Estimation by a Parallel Perceptron for On-Line Selection Hyper-Heuristics

Soria-Alcaraz, J. A., Espinal, A., & Sotelo-Figueroa, M. A. (2017) · IEEE Access · doi:10.1109/access.2017.2699426 · Cited by 8 · see citing works

An Adaptive LS-SVM Based Differential Evolution Algorithm

Yan, X., Wu, M., & Sun, B. (2009) · International Conference on Signal Processing Systems (ICSPS) · doi:10.1109/icsps.2009.129 · Cited by 7 · see citing works

Conditional multichannel generative adversarial networks with an application to traffic signs representation learning

Ghorban, F., Milani, N. S., Schugk, D., Roese-Koerner, L., Su, Y., Müller, D., & Kummert, A. (2018) · Progress in Artificial Intelligence · doi:10.1007/s13748-018-0149-5 · Cited by 7 · see citing works

Applying Algorithm Selection – a Case Study for the Generalised Assignment Problem

Degroote, H., González–Velarde, J. L., & Causmaecker, P. D. (2018) · Electronic Notes in Discrete Mathematics · doi:10.1016/j.endm.2018.07.027 · Cited by 7 · see citing works

A Bee Colony Optimization algorithm with Frequent-closed-pattern-based Pruning Strategy for Traveling Salesman Problem

Wong, L., & Choong, S. S. (2015) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai.2015.7407122 · Cited by 6 · see citing works

Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion AnalysisPreprint

Li, K., & Burdick, J. W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.03592 · Cited by 6 · see citing works

Roster evaluation based on classifiers for the nurse rostering problem

Václavík, R., Šůcha, P., & Hanzálek, Z. (2016) · Journal of Heuristics · doi:10.1007/s10732-016-9314-9 · Cited by 5 · see citing works

Automated Algorithm Configuration: Advances and Prospects

Stützle, T. (2014) · Studies in computational intelligence · doi:10.1007/978-3-319-10422-5_2 · Cited by 4 · see citing works

Introduction to Tensor CalculusPreprint

Goreux, & Sochi, T. (2016) · arXiv (Cornell University) · Cited by 3 · see citing works

Introduction to Tensor CalculusPreprint

Sochi, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.01660 · Cited by 3 · see citing works

An evolutionary cut points search for graph clustering-based discretization

Sriwanna, K., Boongoen, T., & Iam-On, N. (2016) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse.2016.7748929 · Cited by 1 · see citing works

Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art

Karimi-Mamaghan, M., Mohammadi, M., Meyer, P. E., Karimi-Mamaghan, A. M., & Talbi, E. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.04.032 · Cited by 466 · see citing works

Optimization problems for machine learning: A survey

Gambella, C., Ghaddar, B., & Naoum‐Sawaya, J. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.08.045 · Cited by 383 · see citing works

Machine learning to predict end stage kidney disease in chronic kidney disease

Bai, Q., Su, C., Tang, W., & Li, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-12316-z · Cited by 188 · see citing works

Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem

Karimi-Mamaghan, M., Mohammadi, M., Pasdeloup, B., & Meyer, P. E. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2022.03.054 · Cited by 141 · see citing works

An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

Li, B., Wu, G., He, Y., Fan, M., & Pedrycz, W. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105677 · Cited by 110 · see citing works

A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems

Peng, B., Wang, J., & Zhang, Z. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-5577-0_51 · Cited by 91 · see citing works

General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance

Triguero, I., Molina, D., Poyatos, J., Del Ser, J., & Herrera, F. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102135 · Cited by 90 · see citing works

Software engineering challenges for machine learning applications: A literature review

Kumeno, F. (2020) · Intelligent Decision Technologies · doi:10.3233/idt-190160 · Cited by 81 · see citing works

Addressing the class-imbalance and class-overlap problems by a metaheuristic-based under-sampling approach

Soltanzadeh, P., Feizi‐Derakhshi, M., & Hashemzadeh, M. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109721 · Cited by 76 · see citing works

Deep Learning for Proactive Network Monitoring and Security Protection

Nguyen, G., Dlugolinský, Š., Tran, V., & García, Á. L. (2020) · IEEE Access · doi:10.1109/access.2020.2968718 · Cited by 72 · see citing works

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

Barrera-García, J., Cisternas-Caneo, F., Crawford, B., Sánchez, M. G., & Soto, R. (2023) · Biomimetics · doi:10.3390/biomimetics9010009 · Cited by 60 · see citing works

A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems

Asghari, K., Masdari, M., Gharehchopogh, F. S., & Saneifard, R. (2021) · Progress in Artificial Intelligence · doi:10.1007/s13748-021-00244-4 · Cited by 54 · see citing works

Optimization of Machine Learning Algorithms Hyper-Parameters for Improving the Prediction of Patients Infected with COVID-19

Hamida, S., Gannour, O. E., Cherradi, B., Ouajji, H., & Raihani, A. (2020) · IEEE International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) · doi:10.1109/icecocs50124.2020.9314373 · Cited by 48 · see citing works

Nondominated sorting genetic algorithm-II with Q-learning for the distributed permutation flowshop rescheduling problem

Tao, X.-R., Pan, Q.-K., Sang, H., Gao, L., Yang, A.-L., & Miao, R. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110880 · Cited by 45 · see citing works

NeuroCrossover: An intelligent genetic locus selection scheme for genetic algorithm using reinforcement learning

Liu, H., Zong, Z., Li, Y., & Jin, D. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110680 · Cited by 39 · see citing works

Multi-armed bandit-based hyper-heuristics for combinatorial optimization problems

Lagos, F., & Pereira, J. (2023) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.016 · Cited by 36 · see citing works

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification

Le, H. L., Landa-Silva, D., Galar, M., García, S., & Isaac Triguero, A. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107033 · Cited by 34 · see citing works

Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Crawford, B., Soto, R., Lemus-Romani, J., Becerra, M., Lanza-Gutiérrez, J. M., Caballé, N., Castillo, M., Tapia, D., Cisternas-Caneo, F., García, J., et al. (2021) · Mathematics · doi:10.3390/math9161839 · Cited by 33 · see citing works

Designing New Metaheuristics: Manual Versus Automatic Approaches

Villalón, C. L. C., Stützle, T., & Dorigo, M. (2023) · Intelligent Computing · doi:10.34133/icomputing.0048 · Cited by 32 · see citing works

The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement

Hosseini, E., Al-Ghaili, A. M., Kadir, D. H., Daneshfar, F., Gunasekaran, S. S., & Deveci, M. (2024) · IEEE Access · doi:10.1109/access.2024.3452511 · Cited by 30 · see citing works

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities

Angarita-Zapata, J. S., Maestre-Gongora, G. P., & Calderín, J. F. (2021) · Sensors · doi:10.3390/s21248401 · Cited by 30 · see citing works

Decision Support System Applications for Scheduling in Professional Team Sport. The Team's Perspective

Schelling, X., Fernández, J., Ward, P., Fernández, J., & Robertson, S. J. (2021) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2021.678489 · Cited by 29 · see citing works

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

Blackburn, L. D., Tuttle, J. F., Andersson, K., Hedengren, J. D., & Powell, K. M. (2022) · Journal of Process Control · doi:10.1016/j.jprocont.2022.11.002 · Cited by 26 · see citing works

A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants

Samborska-Goik, K., & Pogrzeba, M. (2024) · Applied Sciences · doi:10.3390/app14093675 · Cited by 25 · see citing works

A Novel Learning-Based Binarization Scheme Selector for Swarm Algorithms Solving Combinatorial Problems

Lemus-Romani, J., Becerra, M., Crawford, B., Soto, R., Cisternas-Caneo, F., Vega, E., Castillo, M., Tapia, D., Astorga, G., Palma, W., et al. (2021) · Mathematics · doi:10.3390/math9222887 · Cited by 22 · see citing works

An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem

Sonuç, E., & Özcan, E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119956 · Cited by 21 · see citing works

Embedding Q-Learning in the selection of metaheuristic operators: The enhanced binary grey wolf optimizer case

Tapia, D., Crawford, B., Soto, R., Palma, W., Lemus-Romani, J., Cisternas-Caneo, F., Castillo, M., Becerra, M., Paredes, F., & Misra, S. (2021) · IEEE International Conference on Automatica · doi:10.1109/icaacca51523.2021.9465259 · Cited by 21 · see citing works

Leveraging machine learning and optimization models for enhanced seaport efficiency

Jahangard, M., Xie, Y., & Feng, Y. (2025) · Maritime Economics & Logistics · doi:10.1057/s41278-024-00309-w · Cited by 20 · see citing works

Choosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning

Schweitzer, F., Bitsch, G., & Louw, L. (2023) · Applied Sciences · doi:10.3390/app13020806 · Cited by 20 · see citing works

ML meets MLn: Machine learning in ligand promoted homogeneous catalysis

Hirst, J. D., Boobier, S., Coughlan, J., Streets, J., Jacob, P. L., Pugh, O., Özcan, E., & Woodward, S. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100006 · Cited by 19 · see citing works

A Q-Learning Hyperheuristic Binarization Framework to Balance Exploration and Exploitation

Tapia, D., Crawford, B., Soto, R., Cisternas-Caneo, F., Lemus-Romani, J., Castillo, M., García, J., Palma, W., Paredes, F., & Misra, S. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-61702-8_2 · Cited by 18 · see citing works

Clustering-Based Binarization Methods Applied to the Crow Search Algorithm for 0/1 Combinatorial Problems

Valdivia, S., Soto, R., Crawford, B., Caselli, N., Paredes, F., Castro, C., & Olivares, R. (2020) · Mathematics · doi:10.3390/math8071070 · Cited by 18 · see citing works

Constructing selection hyper-heuristics for open vehicle routing with time delay neural networks using multiple experts

Tyasnurita, R., Özcan, E., Drake, J. H., & Asta, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111731 · Cited by 17 · see citing works

Predicting hydrogen storage in metal-organic frameworks using a novel hybrid machine learning model

Cao, Z.-H., Bian, X., Chen, J., Zhang, J.-Y., & Fu, W. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.06.112 · Cited by 15 · see citing works

Three Steps towards Better Forecasting for Streamflow Deep Learning

Tan, W. Y., Lai, S. H., Teo, F. Y., Armaghani, D. J., Kumar, P., & El‐Shafie, A. (2022) · Applied Sciences · doi:10.3390/app122412567 · Cited by 15 · see citing works

A reinforcement learning-based multiobjective heuristic algorithm for multiple-truck routing problems with heterogeneous drones

Bektur, G. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112290 · Cited by 13 · see citing works

A multimodal deep reinforcement learning approach for IoT-driven adaptive scheduling and robustness optimization in global logistics networks

Lu, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-10512-1 · Cited by 21 · see citing works

Machine Learning and Metaheuristics Approach for Individual Credit Risk Assessment: A Systematic Literature Review

Paz, Á., Crawford, B., Monfroy, É., Barrera-García, J., Peña, Á., Soto, R., Cisternas-Caneo, F., & Yáñez, A. (2025) · Biomimetics · doi:10.3390/biomimetics10050326 · Cited by 12 · see citing works

Self-adaptive General Variable Neighborhood Search algorithm for parallel machine scheduling with unrelated servers

Krimi, I., & Benmansour, R. (2023) · Computers & Operations Research · doi:10.1016/j.cor.2023.106480 · Cited by 12 · see citing works

Subnetwork prediction approach for aircraft schedule recovery

Haider, I. I., Sen, G., Arsalan, M., & Das, A. K. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108472 · Cited by 12 · see citing works

A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Caselli, N., Soto, R., Crawford, B., Valdivia, S., & Olivares, R. (2021) · Mathematics · doi:10.3390/math9161840 · Cited by 12 · see citing works

Joint Approach for Vehicle Routing Problems Based on Genetic Algorithm and Graph Convolutional Network

Qi, D., Zhao, Y., Wang, Z., Wang, W., Pi, L., & Li, L. (2024) · Mathematics · doi:10.3390/math12193144 · Cited by 11 · see citing works

Classification of Urban Green Space Types Using Machine Learning Optimized by Marine Predators Algorithm

Yan, J., Liu, H., Yu, S., Zong, X., & Yao, S. (2023) · Sustainability · doi:10.3390/su15075634 · Cited by 11 · see citing works

Predicting population size and termination criteria in metaheuristics: A case study based on spotted hyena optimizer and crow search algorithm

Vega, E., Soto, R., Crawford, B., Peña, J., Contreras, P., & Castro, C. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109513 · Cited by 9 · see citing works

Swarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector

Becerra, M., Lemus-Romani, J., Cisternas-Caneo, F., Crawford, B., Soto, R., & García, J. (2022) · Mathematics · doi:10.3390/math10244776 · Cited by 9 · see citing works

Intelligent cross-entropy optimizer: A novel machine learning-based meta-heuristic for global optimization

Farahmand‐Tabar, S., & Ashtari, P. (2024) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2024.101739 · Cited by 8 · see citing works

Augmented Humanity

Bryant, P. (2021) · Springer eBooks · doi:10.1007/978-3-030-76445-6 · Cited by 8 · see citing works

A Data-Driven Dynamic Discretization Framework to Solve Combinatorial Problems Using Continuous Metaheuristics

Cisternas-Caneo, F., Crawford, B., Soto, R., de la Fuente‐Mella, H., Tapia, D., Lemus-Romani, J., Castillo, M., Becerra, M., Paredes, F., & Misra, S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-73603-3_7 · Cited by 8 · see citing works

Learnheuristics in routing and scheduling problems: A review

Hussein, A. A., Yassen, E. T., & Rashid, A. N. (2023) · Samarra Journal of Pure and Applied Science · doi:10.54153/sjpas.2023.v5i1.445 · Cited by 9 · see citing works

A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification

Le, H. L., Landa-Silva, D., Galar, M., García, S., & Isaac Triguero, A. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185774 · Cited by 7 · see citing works

A Learning-Based Iterated Local Search Algorithm for Solving the Traveling Salesman Problem

Karimi-Mamaghan, M., Pasdeloup, B., Mohammadi, M., & Meyer, P. E. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-85672-4_4 · Cited by 7 · see citing works

A Learning-Based Hybrid Framework for Dynamic Balancing of Exploration-Exploitation: Combining Regression Analysis and Metaheuristics

Vega, E., Soto, R., Crawford, B., Peña, J., & Castro, C. (2021) · Mathematics · doi:10.3390/math9161976 · Cited by 7 · see citing works

On the Generalized Langevin Equation for Simulated Annealing

Chak, M., Kantas, N., & Pavliotis, G. A. (2023) · SIAM/ASA Journal on Uncertainty Quantification · doi:10.1137/21m1462970 · Cited by 6 · see citing works

Extra dimension algorithm: a breakthrough for optimization and enhancing DNN efficiency

Hosseini, E., Al-Ghaili, A. M., Kadir, D. H., Jamil, N., Deveci, M., Gunasekaran, S. S., & Razali, R. A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10991-0 · Cited by 6 · see citing works

Reinforcement Learning Based Whale Optimizer

Becerra, M., Lemus-Romani, J., Crawford, B., Soto, R., Cisternas-Caneo, F., Embry, A. T., Molina, M. A., Tapia, D., Castillo, M., Misra, S., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87013-3_16 · Cited by 6 · see citing works

Autonomous Parameter Balance in Population-Based Approaches: A Self-Adaptive Learning-Based Strategy

Vega, E., Lemus-Romani, J., Soto, R., Crawford, B., Löffler, C., Peña, J., & Talbi, E.-G. (2024) · Biomimetics · doi:10.3390/biomimetics9020082 · Cited by 5 · see citing works

Whale optimization algorithm and its application in machine learning

Eslami, N., Rahbar, M., Bozorgi, S. M., & Yazdani, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-32-395365-8.00011-7 · Cited by 5 · see citing works

Solving the 0/1 Knapsack Problem Using a Galactic Swarm Optimization with Data-Driven Binarization Approaches

Vásquez, C., Lemus-Romani, J., Crawford, B., Soto, R., Astorga, G., Palma, W., Misra, S., & Paredes, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58817-5_38 · Cited by 5 · see citing works

A Methodology to Determine the Subset of Heuristics for Hyperheuristics through Metalearning for Solving Graph Coloring and Capacitated Vehicle Routing Problems

Ortiz-Aguilar, L. D. M., Carpio, M., Rojas‐Domínguez, A., Ornelas-Rodríguez, M., Soberanes, H. J. P., & Soria-Alcaraz, J. A. (2021) · Complexity · doi:10.1155/2021/6660572 · Cited by 5 · see citing works

Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects

Molina, D., Poyatos, J., Ser, J. D., García, S., Ishibuchi, H., Isaac Triguero, A., Xue, B., Yao, X., & Herrera, F. (2025) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2025.3530096 · Cited by 5 · see citing works

Comparative Analysis of Supervised Machine Learning Algorithms for Predicting Chronic Kidney Diseases

Rizwan, M., Khan, M., Gul, M., Khan, M. A., Naseem, R., & Shaheen, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3102-5_34 · Cited by 4 · see citing works

Cloud Based Heterogeneous Big Data Integration and Data Analysis for Business Intelligence

Jayaraj, T., & Samath, J. A. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-43192-1_101 · Cited by 4 · see citing works

Research on Dynamic Trust Management Mechanism Based on Internet of Vehicles

Yu., S. N., & Chen, Z. (2024) · International Conference on Cyber Security and Information Engineering (ICCSIE) · doi:10.1145/3689236.3689265 · Cited by 3 · see citing works

A New Learnheuristic: Binary SARSA - Sine Cosine Algorithm (BS-SCA)

Becerra, M., Lemus-Romani, J., Crawford, B., Soto, R., Cisternas-Caneo, F., Embry, A. T., Molina, M. A., Tapia, D., Castillo, M., & Rubio, J.-M. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-94216-8_10 · Cited by 3 · see citing works

Combining a Population-Based Approach with Multiple Linear Models for Continuous and Discrete Optimization Problems

Vega, E., Soto, R., Contreras, P., Crawford, B., Peña, J., & Castro, C. (2022) · Mathematics · doi:10.3390/math10162920 · Cited by 3 · see citing works

A transformer-based structure-aware model for tackling the traveling salesman problem

Zhao, C., & Wong, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319711 · Cited by 2 · see citing works

A Method for Road Extraction from High-Resolution Remote Sensing Images Based on Multi-Kernel Learning

Xu, R. Y., & Zeng, Y. (2019) · Information · doi:10.3390/info10120385 · Cited by 2 · see citing works

Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving

Becerra, M., Crawford, B., Soto, R., Talbi, E., & Gomez-Pulido, J. M. (2024) · Biomimetics · doi:10.3390/biomimetics9020089 · Cited by 2 · see citing works

An AutoML-based approach for automatic traffic incident detection in smart cities

Γκιόκα, Γ., Domínguez, M., & Mentzas, G. N. (2024) · Intelligent Decision Technologies · doi:10.3233/idt-240231 · Cited by 2 · see citing works

Intelligent decision-making for binary coverage: Unveiling the potential of the multi-armed bandit selector

Becerra, M., Lemus-Romani, J., Crawford, B., Soto, R., & Talbi, E. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124112 · Cited by 2 · see citing works

Reinforcement Learning for Multi-Neighborhood Local Search in Combinatorial Optimization

Ceschia, S., Gaspero, L. D., Rosati, R. M., & Schaerf, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53966-4_16 · Cited by 2 · see citing works

A Concise Survey on Meta-Learning and Its Applications

Ramkumar, D., Maithili, C., Palaskar, S. P., Toley, M. C., Jadhav, A., & Pande, S. D. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-4359-9_39 · Cited by 1 · see citing works

A First Approach to Refine Semantic Spaces in Zero-Shot Learning with a Genetic Algorithm

Herrera, L. J., Herrera, F., & Isaac Triguero, A. (2025) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec65147.2025.11042972 · Cited by 1 · see citing works

An Ensemble Ant Colony Optimization Algorithm with a Hybrid Pheromone Model for Learning Rule Lists

Brookhouse, J., Helal, A. M., & Otero, F. E. B. (2025) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3712256.3726427 · Cited by 1 · see citing works

An efficient hybridization of Graph Representation Learning and metaheuristics for the Constrained Incremental Graph Drawing Problem

Charytitsch, B. C. B., & Nascimento, M. C. V. (2025) · European Journal of Operational Research · doi:10.1016/j.ejor.2025.08.034 · Cited by 1 · see citing works

Design

Childs, P., & Masen, M. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-22077-7.00015-6 · Cited by 1 · see citing works

Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling

Müller, A., & Vollenkemper, L. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00046 · Cited by 1 · see citing works

Adaptive Optimization of a Dual Moving Average Strategy for Automated Cryptocurrency Trading

Romo, A., Soto, R., Vega, E., Crawford, B., Salinas, A., & Becerra, M. (2025) · Mathematics · doi:10.3390/math13162629 · Cited by 1 · see citing works

Modeling Augmented Humanity

Bryant, P. (2021) · Springer eBooks · doi:10.1007/978-3-030-76445-6_1 · Cited by 1 · see citing works

Investigation of Machine Learning Algorithms for Intrusion Detection System in Cybersecurity

Alrowaily, M. (2020) · Digital Commons - University of South Florida (University of South Florida) · Cited by 1 · see citing works

Using optimization models to achieve solutions in classification and clustering techniques

Quezada, A. J. I. (2020) · doi:10.47749/t/unicamp.2020.1162259 · Cited by 1 · see citing works

Autonomously Improving Systems in Industry: A Systematic Literature Review

Green, R., Bosch, J., & Olsson, H. H. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-67292-8_3

Spatial area determination problem: Definition and solution method based on Memetic Algorithm

Dao, S. D., Mallégol, A., Meyer, P. E., Mohammadi, M., & Loyer, S. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109243

Integrating three-way decisions framework with multiple support vector machines for text classification

Albqmi, A. R. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.235898

Multi-armed Bandit-Based Metaheuristic Operator Selection: The Pendulum Algorithm Binarization Case

Ábrego-Calderón, P., Crawford, B., Soto, R., Rodríguez-Tello, E., Cisternas-Caneo, F., Monfroy, É., & Giachetti, G. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-34020-8_19

Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data

Dave, R., Angarita-Zapata, J. S., & Isaac Triguero, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40837-3_6

A Fitness Approximation Assisted Hyper-heuristic for the Permutation Flowshop Problem

Cherrered, A., Mekki, I. R., Benatchba, K., & Tayeb, F. B.-S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-41774-0_42

Um estudo sobre classificação binária baseado em programação matemática

Ribeiro, L. Z. (2022) · doi:10.47749/t/unicamp.2022.1261605

Investigation on the Structure Profile of Precise Copper Tube by Three-Roll Planetary Rolling Based on Finite Element Simulation Assisted by Machine Learning

Liu, J., Sun, Y.-L., Chen, D.-Y., Song, H.-W., Zhang, S., & Chen, C.-L. (2024) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-024-10380-2

Metaheuristics for Automating Performance Measurement in Management

Paramasamy, S., Rajeswari, A. M., Alabdeli, H. M., Mahalakshmi, R., Raju, Y. K., & Supreeth, B. R. (2025) · International Conference on Intelligent Control, Computing and Communications (IC3) · doi:10.1109/ic363308.2025.10956175

Student Surpasses the Teacher: Apprenticeship Learning for Quadratic Unconstrained Binary Optimisation

Cakebread, J., Jackson, W. G., Karapetyan, D., Parkes, A. J., & Özcan, E. (2025) · Algorithms · doi:10.3390/a18080516

A systematic review of the literature on recent trends in dynamic combinatorial optimization problems linked to meta-learning in metaheuristics and quantum computing

Jiménez, M., Aguilar, J., Montoya, J., & Montoya, E. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07792-9

Short-Term Hourly Hydropower Prediction: Evaluating Long Short-Term Memory (LSTM) and Mixed-Integer Linear Programming (MILP)-Based MethodsPreprint

Villeneuve, Y., Séguin, S., Chehri, A., & Demeester, K. (2025) · Research Square · doi:10.21203/rs.3.rs-8535183/v1

Microservice provisioning and event-driven adaptation in heterogeneous IoT settings

Li, Y., Liu, F., Chang, T.-Y., Hsu, C.-H., & Venkatasubramanian, N. (2026) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2026.102195

The interplay of machine learning and metaheuristics

Rakhshani, H. (2020) · doi:10.70675/5209ef77z0160z4f33zaa43z924bb47d2221

Hybridizing metaheuristics with machine learning for combinatorial optimization : a taxonomy and learning to select operators

Karimi-Mamaghan, M. (2022) · doi:10.70675/51fe5b19z8286z4d4fzb492z91b9038a9553

Reinforcement learning-enhanced multi-strategy cuckoo search algorithm to solve combined economic emission dispatch problems

Xu, P., & Yu, X. (2026) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2026.104188

Cross-domain performance analysis of meme selection strategies in multi-meme memetic algorithms

Alkaya, A. F., & Özcan, E. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115274

Landscape-driven stochastic variation: A Data-Driven Meta-Learning Framework for Adaptive Mutation Control (DD-AMC) in constrained evolutionary multi-objective optimisation

Abidallah, N., Bourekkache, S., Kahloul, L., Atia, O. B., Khalgui, M., & Lorenz, P. (2026) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2026.102535

The NAL-NL3 Prescription

Kitterick, P. T., Monaghan, J. J. M., Zakis, J. A., Gavrilis, J., Croteau, M., Mishra, D., Boothalingam, S., Kwok, C., Rutledge, K. L., Gilliver, M., et al. (2026) · International Journal of Audiology · doi:10.1080/14992027.2026.2743347

How Can Machine Learning and Optimization Help Each Other Better?

Lin, Z. (2019) · Journal of the Operations Research Society of China · doi:10.1007/s40305-019-00285-6 · Cited by 4 · see citing works

Optimisation and supervised learning for decision making: competitors or partners?

Mor, A., Orsenigo, C., & Speranza, M. G. (2026) · Journal of the Operational Research Society · doi:10.1080/01605682.2026.2616419 · Cited by 1 · see citing works

Machine learning-enhanced hybrid optimization: A critical review and research agenda

Tuib, T. A. (2026) · International Journal of Engineering in Computer Science · doi:10.33545/26633582.2026.v8.i1b.265

Optimization for Machine Learning

Sra, S., Nowozin, S., & Wright, S. J. (2011) · MPG.PuRe (Max Planck Society)

Machine Learning and Optimization

Milzarek, A., Wen, Z.-W., & Yang, J. (2026) · Optimization and Engineering · doi:10.1007/s11081-026-10112-6 · Cited by 1 · see citing works