| A brief review of nature-inspired algorithms for optimization I Fister Jr, XS Yang, I Fister, J Brest, D Fister arXiv preprint arXiv:1307.4186, 2013 | 1001 | 2013 |
| A hybrid bat algorithm I Fister Jr, D Fister, XS Yang arXiv preprint arXiv:1303.6310, 2013 | 246 | 2013 |
| Cuckoo search: a brief literature review I Fister, XS Yang, D Fister, I Fister Cuckoo search and firefly algorithm: Theory and applications, 49-62, 2014 | 188 | 2014 |
| A comprehensive review of cuckoo search: variants and hybrids I Fister Jr, D Fister, I Fister International Journal of Mathematical Modelling and Numerical Optimisation 4 …, 2013 | 130 | 2013 |
| Parameter tuning of PID controller with reactive nature-inspired algorithms D Fister, I Fister Jr, I Fister, R Šafarič Robotics and Autonomous Systems 84, 64-75, 2016 | 94 | 2016 |
| NiaPy: Python microframework for building nature-inspired algorithms G Vrbančič, L Brezočnik, U Mlakar, D Fister, I Fister Journal of Open Source Software 3 (23), 613, 2018 | 86 | 2018 |
| Firefly algorithm: a brief review of the expanding literature I Fister, XS Yang, D Fister, I Fister Cuckoo Search and Firefly Algorithm: Theory and Applications, 347-360, 2014 | 77 | 2014 |
| Data mining in sporting activities created by sports trackers I Fister Jr, D Fister, I Fister, S Fong Computational and Business Intelligence (ISCBI), 2013 International …, 2013 | 49 | 2013 |
| Computational intelligence in sports I Fister, I Fister Jr, D Fister Springer, 2019 | 42 | 2019 |
| Post hoc analysis of sport performance with differential evolution I Fister, D Fister, S Deb, U Mlakar, J Brest, I Fister Neural Computing and Applications 32, 10799-10808, 2020 | 32 | 2020 |
| A brief review of nature-inspired algorithms for optimization. arXiv 2013 I Fister Jr, XS Yang, I Fister, J Brest, D Fister arXiv preprint arXiv:1307.4186, 0 | 31 | |
| Accurate long-term air temperature prediction with machine learning models and data reduction techniques D Fister, J Pérez-Aracil, C Peláez-Rodríguez, J Del Ser, S Salcedo-Sanz Applied Soft Computing 136, 110118, 2023 | 30 | 2023 |
| Deep learning for stock market trading: a superior trading strategy? D Fister, JC Mun, V Jagrič, T Jagrič Neural Network World, 2019 | 30 | 2019 |
| Two robust long short-term memory frameworks for trading stocks D Fister, M Perc, T Jagrič Applied Intelligence 51 (10), 7177-7195, 2021 | 29 | 2021 |
| Generating eating plans for athletes using the particle swarm optimization D Fister, I Fister, S Rauter 2016 IEEE 17th International Symposium on Computational Intelligence and …, 2016 | 29 | 2016 |
| Analysis of randomisation methods in swarm intelligence IF Jr, XS Yang, J Brest, D Fister, I Fister International journal of bio-inspired computation 7 (1), 36-49, 2015 | 28 | 2015 |
| Towards the novel reasoning among particles in PSO by the use of RDF and SPARQL I Fister Jr, XS Yang, K Ljubič, D Fister, J Brest, I Fister The Scientific World Journal 2014 (1), 121782, 2014 | 24 | 2014 |
| Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review S Salcedo-Sanz, J Pérez-Aracil, G Ascenso, J Del Ser, D Casillas-Pérez, ... Theoretical and Applied Climatology 155 (1), 1-44, 2024 | 23 | 2024 |
| Planning Fitness Training Sessions Using the Bat Algorithm. I Fister Jr, S Rauter, KL Fister, D Fister, I Fister ITAT, 121-126, 2015 | 21 | 2015 |
| Differential evolution strategies with random forest regression in the bat algorithm I Fister Jr, D Fister, I Fister Proceedings of the 15th annual conference companion on Genetic and …, 2013 | 21 | 2013 |