72 citations as recorded by crossref.

1. TRIDENT-DE: Triple-Operator Differential Evolution with Adaptive Restarts and Greedy Refinement V. Charilogis et al. https://doi.org/10.3390/fi17110488
2. Training Artificial Neural Networks Using a Global Optimization Method That Utilizes Neural Networks I. Tsoulos & A. Tzallas https://doi.org/10.3390/ai4030027
3. Random Bits Forest: a Strong Classifier/Regressor for Big Data Y. Wang et al. https://doi.org/10.1038/srep30086
4. Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches D. Kafka & D. Wilke https://doi.org/10.1007/s10898-020-00921-z
5. Optimizing the Bounds of Neural Networks Using a Novel Simulated Annealing Method I. Tsoulos et al. https://doi.org/10.3390/appliedmath6020023
6. The parametric sensitivity of CAM5's MJO J. Boyle et al. https://doi.org/10.1002/2014JD022507
7. Learning Functions and Classes Using Rules I. Tsoulos https://doi.org/10.3390/ai3030044
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10. Value proposition operationalization in peer-to-peer platforms using machine learning J. Ramos-Henríquez et al. https://doi.org/10.1016/j.tourman.2021.104288
11. Train Neural Networks with a Hybrid Method That Incorporates a Novel Simulated Annealing Procedure I. Tsoulos et al. https://doi.org/10.3390/appliedmath4030061
12. A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution I. Tsoulos & A. Tzallas https://doi.org/10.3390/app13148124
13. Random bits regression: a strong general predictor for big data Y. Wang et al. https://doi.org/10.1186/s41044-016-0010-4
14. Reduced order models for assessing CO2 impacts in shallow unconfined aquifers E. Keating et al. https://doi.org/10.1016/j.ijggc.2016.01.008
15. Bound the Parameters of Neural Networks Using Particle Swarm Optimization I. Tsoulos et al. https://doi.org/10.3390/computers12040082
16. Exploring High‐Dimensional Structure via Axis‐Aligned Decomposition of Linear Projections J. Thiagarajan et al. https://doi.org/10.1111/cgf.13416
17. A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF C. Marzban et al. https://doi.org/10.1175/MWR-D-19-0271.1
18. Improving the Generalization Abilities of Constructed Neural Networks with the Addition of Local Optimization Techniques I. Tsoulos et al. https://doi.org/10.3390/a17100446
19. Tackling Climate Change with Machine Learning D. Rolnick et al. https://doi.org/10.1145/3485128
20. A multivariate analysis and machine learning approach to assess the impact of climate change on water quality N. Salehi Siavashani et al. https://doi.org/10.1007/s40899-026-01336-6
21. Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation I. Yamane et al. https://doi.org/10.1162/NECO_a_00844
22. From Initialization to Convergence: A Three-Stage Technique for Robust RBF Network Training I. Tsoulos et al. https://doi.org/10.3390/ai6110280
23. QFC: A Parallel Software Tool for Feature Construction, Based on Grammatical Evolution I. Tsoulos https://doi.org/10.3390/a15080295
24. Improving the Performance of Constructed Neural Networks with a Pre-Train Phase I. Tsoulos et al. https://doi.org/10.3390/sym17091567
25. Neural DE: An Evolutionary Method Based on Differential Evolution Suitable for Neural Network Training I. Tsoulos & V. Charilogis https://doi.org/10.3390/appliedmath5010027
26. Constructing Features Using a Hybrid Genetic Algorithm I. Tsoulos https://doi.org/10.3390/signals3020012
27. Low regularity exponential-type integrators for the “good” Boussinesq equation H. Li & C. Su https://doi.org/10.1093/imanum/drac081
28. Gen2Gen: Efficiently Training Artificial Neural Networks Using a Series of Genetic Algorithms I. Tsoulos & V. Charilogis https://doi.org/10.3390/knowledge5030017
29. Deep learning with support vector data description S. Kim et al. https://doi.org/10.1016/j.neucom.2014.09.086
30. Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges W. Paja https://doi.org/10.3390/e25081223
31. Ensemble simulations of inertial confinement fusion implosions R. Nora et al. https://doi.org/10.1002/sam.11344
32. The examination of the effect of the criterion for neural network’s learning on the effectiveness of the qualitative analysis of multidimensional data D. Jamróz https://doi.org/10.1007/s10115-020-01441-8
33. RbfCon: Construct Radial Basis Function Neural Networks with Grammatical Evolution I. Tsoulos et al. https://doi.org/10.3390/software3040027
34. RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification A. Arafa et al. https://doi.org/10.1016/j.jksuci.2022.06.005
35. Introducing an Evolutionary Method to Create the Bounds of Artificial Neural Networks I. Tsoulos et al. https://doi.org/10.3390/foundations5020011
36. An automated design process for short pulse laser driven opacity experiments M. Martin et al. https://doi.org/10.1016/j.hedp.2017.12.001
37. RFCL: A new under-sampling method of reducing the degree of imbalance and overlap R. Zhang et al. https://doi.org/10.1007/s10044-020-00929-x
38. A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques P. Chen et al. https://doi.org/10.1177/1550147718777440
39. Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant D. Lucas et al. https://doi.org/10.5194/acp-17-13521-2017
40. Adapting the Parameters of RBF Networks Using Grammatical Evolution I. Tsoulos et al. https://doi.org/10.3390/ai4040054
41. Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method I. Tsoulos & V. Charilogis https://doi.org/10.3390/a16020071
42. Training Neural Networks with a Procedure Guided by BNF Grammars I. Tsoulos  & V. Charilogis https://doi.org/10.3390/bdcc9010005
43. Sensitivity Analysis of the Spatial Structure of Forecasts in Mesoscale Models: Noncontinuous Model Parameters C. Marzban et al. https://doi.org/10.1175/MWR-D-19-0321.1
44. A Two-Phase Evolutionary Method to Train RBF Networks I. Tsoulos et al. https://doi.org/10.3390/app12052439
45. Local Crossover: A New Genetic Operator for Grammatical Evolution I. Tsoulos et al. https://doi.org/10.3390/a17100461
46. Using Optimization Techniques in Grammatical Evolution I. Tsoulos et al. https://doi.org/10.3390/fi16050172
47. Combining Constructed Artificial Neural Networks with Parameter Constraint Techniques to Achieve Better Generalization Properties I. Tsoulos et al. https://doi.org/10.3390/sym17091557
48. A Review and Experimental Comparison of Multivariate Decision Trees L. Canete-Sifuentes et al. https://doi.org/10.1109/ACCESS.2021.3102239
49. An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers U. Garciarena & R. Santana https://doi.org/10.1016/j.eswa.2017.07.026
50. On the effect of model parameters on forecast objects C. Marzban et al. https://doi.org/10.5194/gmd-11-1577-2018
51. To what extent does ENSO rectify the tropical Pacific mean state? M. Xue & T. Li https://doi.org/10.1007/s00382-023-06750-6
52. What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models R. Sheikholeslami et al. https://doi.org/10.5194/gmd-12-4275-2019
53. Utilizing a Bounding Procedure Based on Simulated Annealing to Effectively Locate the Bounds for the Parameters of Radial Basis Function Networks I. Tsoulos et al. https://doi.org/10.3390/a18040234
54. A Rule-Based Method to Locate the Bounds of Neural Networks I. Tsoulos et al. https://doi.org/10.3390/knowledge2030024
55. Finding plausible and diverse variants of a climate model. Part II: development and validation of methodology A. Karmalkar et al. https://doi.org/10.1007/s00382-019-04617-3
56. Approximation trees: statistical reproducibility in model distillation Y. Zhou et al. https://doi.org/10.1007/s10618-022-00907-3
57. Healing Intelligence: A Bio-Inspired Metaheuristic Optimization Method Using Recovery Dynamics V. Charilogis & I. Tsoulos https://doi.org/10.3390/fi17100441
58. A genetic algorithm approach to optimising random forests applied to class engineered data E. Elyan & M. Gaber https://doi.org/10.1016/j.ins.2016.08.007
59. Calibrated simplex-mapping classification R. Heese et al. https://doi.org/10.1371/journal.pone.0279876
60. Low-regularity integrators for the “good” Boussinesq equation with linearly decreasing additional derivative requirements H. Li & C. Su https://doi.org/10.1142/S0218202525500381
61. Earth system simulations suggest that the Proterozoic ocean was greener but less productive P. Liu et al. https://doi.org/10.1038/s41467-026-69654-z
62. Applying Bounding Techniques on Grammatical Evolution I. Tsoulos et al. https://doi.org/10.3390/computers13050111
63. Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models W. Paja et al. https://doi.org/10.5194/gmd-9-1065-2016
64. Extremely simple classifier based on fuzzy logic and gene expression programming J. Kluska & M. Madera https://doi.org/10.1016/j.ins.2021.05.041
65. Evaluation of scale-aware subgrid mesoscale eddy models in a global eddy-rich model B. Pearson et al. https://doi.org/10.1016/j.ocemod.2017.05.007
66. Uncertainty Analysis of Simulations of the Turn‐of‐the‐Century Drought in the Western United States G. Anderson et al. https://doi.org/10.1029/2017JD027824
67. Constructing the Bounds for Neural Network Training Using Grammatical Evolution I. Tsoulos et al. https://doi.org/10.3390/computers12110226
68. A novel Bayesian method for variable selection and estimation in binary quantile regression M. Dao et al. https://doi.org/10.1002/sam.11591
69. Multi-Layer Hybrid (MLH) balancing technique: A combined approach to remove data imbalance M. Islam & H. Mustafa https://doi.org/10.1016/j.datak.2022.102105
70. New Approach Based on Termite's Hill Building for Prediction of Successful Simulations in Climate Models M. Rahmani et al. https://doi.org/10.4018/IJSIR.2017070103
71. Introducing a New Genetic Operator Based on Differential Evolution for the Effective Training of Neural Networks I. Tsoulos et al. https://doi.org/10.3390/computers14040125
72. Machine Learning Predictions of a Multiresolution Climate Model Ensemble G. Anderson & D. Lucas https://doi.org/10.1029/2018GL077049