Dr Fabio Caraffini

Associate Professor

Computer Science

fabio.caraffini@swansea.ac.uk

Available For Postgraduate Supervision

Research Links

https://orcid.org/0000-0001-9199-7368

ResearchGate

Google Scholar

Scopus

About

Dr Fabio Caraffini's research includes fundamental and applied computational intelligence with a strong emphasis on heuristics for optimisation.

He holds a PhD in Mathematical Information Technology (University of Jyväskylä, Finland, 2016) and Computer Science (De Montfort University, UK, 2014), and received the BSc in Electronics Engineering and the MSc in Telecommunications Engineering degrees from the University of Perugia (Italy) in 2008 and 2011 respectively.

Web of Science ResearcherID

Areas Of Expertise

Evolutionary Computing
Heuristic & Hyperheuristic Optimisation
Algorithm Behaviour
AI-driven cell network analysis

Career Highlights

Teaching Interests

I am a Fellow of the Higher Education Academy (FHEA).

Before joining the Department of Computer Science at Swansea University, I designed and delivered level 4 modules such as 'Calculus I' and 'Mathematical Analysis', as well as level 7 modules such as 'Computational Intelligence Optimisation'.

Research Award Highlights

Publications

Research Highlights

Caraffini, F., Kononova, A., & Corne, D. (2019). Infeasibility and structural bias in differential evolution. Information Sciences, 496, 161-179.
https://doi.org/10.1016/j.ins.2019.05.019, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60938
Caraffini, F., Eshkiki, H., Mohammadpour, M., Sullo, N., & George, C. (2024). Towards Improving Single-Cell Segmentation in Heterogeneous Configurations of Cardiomyocyte Networks. In Lecture Notes in Computer Science (pp. 104-117). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67383
Vermetten, D., Stein, B., Caraffini, F., Minku, L., & Kononova, A. (2022). BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain. IEEE Transactions on Evolutionary Computation, 1-1.
https://doi.org/10.1109/tevc.2022.3189848, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60904
Custode, L., Caraffini, F., Yaman, A., & Iacca, G. (2024). An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1838-1845). ACM.
https://doi.org/10.1145/3638530.3664163, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67311
Kononova, A., Vermetten, D., Caraffini, F., Mitran, M., & Zaharie, D. (2024). The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond. Evolutionary Computation, 32(1), 3-48.
https://doi.org/10.1162/evco_a_00333, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63489
Florez-Lozano, J., Caraffini, F., Parra, C., & Gongora, M. (2020). Cooperative and distributed decision-making in a multi-agent perception system for improvised land mines detection. Information Fusion, 64, 32-49.
https://doi.org/10.1016/j.inffus.2020.06.009, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60949
Mitran, M., Kononova, A., Caraffini, F., & Zaharie, D. (2023). Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking Suite. In Proceedings of the Companion Conference on Genetic and Evolutionary ComputationACM.
https://doi.org/10.1145/3583133.3596410, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63991
http://dx.doi.org/10.1145/3583133.3596410
Stein, B., Vermetten, D., Caraffini, F., & Kononova, A. (2023). Deep BIAS: Detecting Structural Bias using Explainable AI. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationACM.
https://doi.org/10.1145/3583133.3590551, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63992
http://dx.doi.org/10.1145/3583133.3590551
Bonet, I., Caraffini, F., Pena, A., Puerta, A., & Gongora, M. (2020). Oil Palm Detection via Deep Transfer Learning. In 2020 IEEE Congress on Evolutionary Computation (CEC)IEEE.
https://doi.org/10.1109/cec48606.2020.9185838, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60947
Peña, A., Puerta, A., Bonet, I., Caraffini, F., Gongora, M., & Ochoa, I. (2023). A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops. Applications of Evolutionary Computation (pp. 491-506). Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-30229-9_32, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63100
http://dx.doi.org/10.1007/978-3-031-30229-9_32

All Research

Journal Articles

Finnegan, R., Metcalfe, J., Sharifzadeh, S., Caraffini, F., Xie, X., Hornero, A., & Synes, N. (2025). Temporal Optimisation of Satellite Image‐Based Crop Mapping: A Comparison of Deep Time Series and Semi‐Supervised Time Warping Strategies. IET Computer Vision, 19(1), e70014
https://doi.org/10.1049/cvi2.70014, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa69047
Abdullah, M., Hongying, Z., Javed, A., Mamyrbayev, O., Caraffini, F., & Eshkiki, H. (2025). A joint learning framework for fake news detection. Displays, 90, 103154
https://doi.org/10.1016/j.displa.2025.103154, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa70027
Akbar, A., Han, S., Rehman, N., Ahmed, K., Eshkiki, H., & Caraffini, F. (2025). Explainable breast cancer prediction from 3-dimensional dynamic contrast-enhanced magnetic resonance imaging. Applied Intelligence, 55(13)
https://doi.org/10.1007/s10489-025-06803-9, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa70043
Pérez-Pérez, J., Bonet, I., Sánchez-Pinzón, M., Caraffini, F., & Lochmuller, C. (2024). Using Artificial Intelligence to Predict the Financial Impact of Climate Transition Risks Within Organisations. International Journal of Intelligent Systems, 2024(1), 3334263
https://doi.org/10.1155/int/3334263, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa68573
Pérez-Pérez, J., Gómez, P., Bonet, I., Sánchez-Pinzón, M., Caraffini, F., & Lochmuller, C. (2024). Assessing Climate Transition Risks in the Colombian Processed Food Sector: A Fuzzy Logic and Multi-Criteria Decision-Making Approach. Mathematics, 12(17), 2713
https://doi.org/10.3390/math12172713, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67548
Rhazzafe, S., Caraffini, F., Colreavy-Donnelly, S., Dhassi, Y., Kuhn, S., & Nikolov, N. (2024). Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit. Applied Sciences, 14(13), 5809
https://doi.org/10.3390/app14135809, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa66965
Kononova, A., Vermetten, D., Caraffini, F., Mitran, M., & Zaharie, D. (2024). The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond. Evolutionary Computation, 32(1), 3-48.
https://doi.org/10.1162/evco_a_00333, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63489
Khalfi, S., Caraffini, F., & Iacca, G. (2023). Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources. International Journal of Intelligent Systems, 2023, 1-32.
https://doi.org/10.1155/2023/5708085, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64958
http://dx.doi.org/10.1155/2023/5708085
Morden, J., Caraffini, F., Kypraios, I., Al-Bayatti, A., & Smith, R. (2023). Driving in the Rain: A Survey toward Visibility Estimation through Windshields. International Journal of Intelligent Systems, 2023, 1-26.
https://doi.org/10.1155/2023/9939174, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64331
http://dx.doi.org/10.1155/2023/9939174
Kuhn, S., Cobas, C., Barba, A., Colreavy-Donnelly, S., Caraffini, F., & Borges, R. (2023). Direct deduction of chemical class from NMR spectra. Journal of Magnetic Resonance, 348, 107381
https://doi.org/10.1016/j.jmr.2023.107381, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62394
Kim, Y. & Caraffini, F. (2023). Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”. Mathematics, 11(5), 1209
https://doi.org/10.3390/math11051209, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62775
Pena, A., Tejada, J., Gonzalez-Ruiz, J., Sepúlveda-Cano, L., Chiclana, F., Caraffini, F., & Gongora, M. (2023). An evolutionary intelligent control system for a flexible joints robot. Applied Soft Computing, 135, 110043
https://doi.org/10.1016/j.asoc.2023.110043, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62420
Fox, M., Yang, S., & Caraffini, F. (2022). A new moving peaks benchmark with attractors for dynamic evolutionary algorithms. Swarm and Evolutionary Computation, 74, 101125
https://doi.org/10.1016/j.swevo.2022.101125, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60896
Su, L., Homapour, E., Caraffini, F., & Chiclana, F. (2022). An Empirical Investigation of Multinationality and Stock Price Crash Risk for MNCs in China. Mathematics, 10(19), 3464
https://doi.org/10.3390/math10193464, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61281
Ryan, L., Kuhn, S., Colreavy-Donnely, S., & Caraffini, F. (2022). Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System. Applied Sciences, 12(15), 7827
https://doi.org/10.3390/app12157827, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60897
Izzard, J., Caraffini, F., & Chiclana, F. (2022). Towards a software tool for general meal optimisation. Applied Intelligence
https://doi.org/10.1007/s10489-022-03935-0, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60898
Vermetten, D., Stein, B., Caraffini, F., Minku, L., & Kononova, A. (2022). BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain. IEEE Transactions on Evolutionary Computation, 1-1.
https://doi.org/10.1109/tevc.2022.3189848, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60904
Colreavy-Donelly, S., Ryan, A., O’Connor, S., Caraffini, F., Kuhn, S., & Hasshu, S. (2022). A Proposed VR Platform for Supporting Blended Learning Post COVID-19. Education Sciences, 12(7), 435
https://doi.org/10.3390/educsci12070435, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60899
Moodley, R., Chiclana, F., Caraffini, F., & Gongora, M. (2022). Using self‐organising maps to predict and contain natural disasters and pandemics. International Journal of Intelligent Systems, 37(4), 2739-2757.
https://doi.org/10.1002/int.22440, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60901
Caraffini, F., Chiclana, F., Moodley, R., & Gongora, M. (2022). Applications of computational intelligence‐based systems for societal enhancement. International Journal of Intelligent Systems, 37(4), 2679-2682.
https://doi.org/10.1002/int.22862, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60900
Homapour, E., Su, L., Caraffini, F., & Chiclana, F. (2022). Regression Analysis of Macroeconomic Conditions and Capital Structures of Publicly Listed British Firms. Mathematics, 10(7), 1119
https://doi.org/10.3390/math10071119, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60902
Kononova, A., Caraffini, F., & Bäck, T. (2021). Differential evolution outside the box. Information Sciences, 581, 587-604.
https://doi.org/10.1016/j.ins.2021.09.058, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60960
Agresta, A., Baioletti, M., Biscarini, C., Caraffini, F., Milani, A., & Santucci, V. (2021). Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis. Applied Sciences, 11(22), 10575
https://doi.org/10.3390/app112210575, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60906
O’Connor, S., Hasshu, S., Bielby, J., Colreavy-Donnelly, S., Kuhn, S., Caraffini, F., & Smith, R. (2021). SCIPS: A serious game using a guidance mechanic to scaffold effective training for cyber security. Information Sciences, 580, 524-540.
https://doi.org/10.1016/j.ins.2021.08.098, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60959
Pena, A., Patino, A., Chiclana, F., Caraffini, F., Gongora, M., Gonzalez-Ruiz, J., & Duque-Grisales, E. (2021). Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events. Applied Soft Computing, 107, 107381
https://doi.org/10.1016/j.asoc.2021.107381, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60907
Khishe, M., Caraffini, F., & Kuhn, S. (2021). Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images. Mathematics, 9(9), 1002
https://doi.org/10.3390/math9091002, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60908
Florez-Lozano, J., Caraffini, F., Parra, C., & Gongora, M. (2020). Cooperative and distributed decision-making in a multi-agent perception system for improvised land mines detection. Information Fusion, 64, 32-49.
https://doi.org/10.1016/j.inffus.2020.06.009, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60949
Iacca, G. & Caraffini, F. (2020). Re-sampled inheritance compact optimization. Knowledge-Based Systems, 208, 106416
https://doi.org/10.1016/j.knosys.2020.106416, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60944
Colreavy-Donnelly, S., Caraffini, F., Kuhn, S., Gongora, M., Florez-Lozano, J., & Parra, C. (2020). Shallow buried improvised explosive device detection via convolutional neural networks. Integrated Computer-Aided Engineering, 27(4), 403-416.
https://doi.org/10.3233/ica-200638, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60943
Florez-Lozano, J., Caraffini, F., Parra, C., & Gongora, M. (2020). A Robust Decision-Making Framework Based on Collaborative Agents. IEEE Access, 8, 150974-150988.
https://doi.org/10.1109/access.2020.3016784, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60953
Caraffini, F. & Iacca, G. (2020). The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms. Mathematics, 8(5), 785
https://doi.org/10.3390/math8050785, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60958
Moodley, R., Chiclana, F., Carter, J., & Caraffini, F. (2020). Using Data Mining in Educational Administration: A Case Study on Improving School Attendance. Applied Sciences, 10(9), 3116
https://doi.org/10.3390/app10093116, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60957
Kosti, A., Colreavy-Donnelly, S., Caraffini, F., & Anastassi, Z. (2020). Efficient Computation of the Nonlinear Schrödinger Equation with Time-Dependent Coefficients. Mathematics, 8(3), 374
https://doi.org/10.3390/math8030374, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60956
Yeoh, J., Caraffini, F., Homapour, E., Santucci, V., & Milani, A. (2019). A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation. Mathematics, 7(12), 1229
https://doi.org/10.3390/math7121229, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60941
Santucci, V., Milani, A., & Caraffini, F. (2019). An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis. Mathematics, 7(11), 1051
https://doi.org/10.3390/math7111051, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60940
Caraffini, F. & Neri, F. (2019). A study on rotation invariance in differential evolution. Swarm and Evolutionary Computation, 50, 100436
https://doi.org/10.1016/j.swevo.2018.08.013, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60939
Caraffini, F., Kononova, A., & Corne, D. (2019). Infeasibility and structural bias in differential evolution. Information Sciences, 496, 161-179.
https://doi.org/10.1016/j.ins.2019.05.019, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60938
Caraffini, F., Neri, F., & Epitropakis, M. (2019). HyperSPAM: A study on hyper-heuristic coordination strategies in the continuous domain. Information Sciences, 477, 186-202.
https://doi.org/10.1016/j.ins.2018.10.033, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63385
http://dx.doi.org/10.1016/j.ins.2018.10.033
Moodley, R., Chiclana, F., Caraffini, F., & Carter, J. (2019). Application of uninorms to market basket analysis. International Journal of Intelligent Systems, 34(1), 39-49.
https://doi.org/10.1002/int.22039, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62441
Kononova, A., Corne, D., Wilde, P., Shneer, V., & Caraffini, F. (2015). Structural bias in population-based algorithms. Information Sciences, 298, 468-490.
https://doi.org/10.1016/j.ins.2014.11.035, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60927
Poikolainen, I., Neri, F., & Caraffini, F. (2015). Cluster-Based Population Initialization for differential evolution frameworks. Information Sciences, 297, 216-235.
https://doi.org/10.1016/j.ins.2014.11.026, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60930
Cheng, J., Zhang, G., Caraffini, F., & Neri, F. (2015). Multicriteria adaptive differential evolution for global numerical optimization. Integrated Computer-Aided Engineering, 22(2), 103-107.
https://doi.org/10.3233/ica-150481, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60928
Caraffini, F., Neri, F., & Picinali, L. (2014). An analysis on separability for Memetic Computing automatic design. Information Sciences, 265, 1-22.
https://doi.org/10.1016/j.ins.2013.12.044, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60925
IACCA, G., Caraffini, F., & NERI, F. (2014). MULTI-STRATEGY COEVOLVING AGING PARTICLE OPTIMIZATION. International Journal of Neural Systems, 24(01), 1450008
https://doi.org/10.1142/s0129065714500087, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60924
Caraffini, F., Neri, F., Passow, B., & Iacca, G. (2013). Re-sampled inheritance search: high performance despite the simplicity. Soft Computing, 17(12), 2235-2256.
https://doi.org/10.1007/s00500-013-1106-7, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60918
Poikolainen, I., Iacca, G., Caraffini, F., & Neri, F. (2013). Focusing the search: a progressively shrinking memetic computing framework. International Journal of Innovative Computing and Applications, 5(3), 127
https://doi.org/10.1504/ijica.2013.055929, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60922
Caraffini, F., Neri, F., Iacca, G., & Mol, A. (2013). Parallel memetic structures. Information Sciences, 227, 60-82.
https://doi.org/10.1016/j.ins.2012.11.017, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60919
Iacca, G., Caraffini, F., & Neri, F. (2013). Memory-saving memetic computing for path-following mobile robots. Applied Soft Computing, 13(4), 2003-2016.
https://doi.org/10.1016/j.asoc.2012.11.039, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60921
Iacca, G., Caraffini, F., & Neri, F. (2012). Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead. Journal of Computer Science and Technology, 27(5), 1056-1076.
https://doi.org/10.1007/s11390-012-1284-2, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60914

Book Chapters

COSTA, S., Eshkiki, H., Caraffini, F., & George, C. (2026). Enhancing Cardiac Cell Networks Segmentation via Hybrid Supervised and Zero-Shot Strategies. Lecture Notes in Computer Science (pp. 351-365). Springer Nature Switzerland
https://doi.org/10.1007/978-3-032-00656-1_26, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa69568
https://doi.org/10.1007/978-3-032-00656-1_26
Peña, A., Puerta, A., Bonet, I., Caraffini, F., Gongora, M., & Ochoa, I. (2023). A Multispectral Image Classification Framework for Estimating the Operational Risk of Lethal Wilt in Oil Palm Crops. Applications of Evolutionary Computation (pp. 491-506). Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-30229-9_32, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63100
http://dx.doi.org/10.1007/978-3-031-30229-9_32
Puerta, A., Hoyos, S., Bonet, I., & Caraffini, F. (2023). An AI-Based Support System for Microgrids Energy Management. Applications of Evolutionary Computation (pp. 507-518). Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-30229-9_33, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63101
http://dx.doi.org/10.1007/978-3-031-30229-9_33
Agresta, A., Biscarini, C., Caraffini, F., & Santucci, V. (2023). An Intelligent Optimised Estimation of the Hydraulic Jump Roller Length. Applications of Evolutionary Computation (pp. 475-490). Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-30229-9_31, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63102
http://dx.doi.org/10.1007/978-3-031-30229-9_31
Vermetten, D., Stein, B., Kononova, A., & Caraffini, F. (2022). Analysis of Structural Bias in Differential Evolution Configurations. Studies in Computational Intelligence (pp. 1-22). Springer Nature Singapore
https://doi.org/10.1007/978-981-16-8082-3_1, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60903

Conference Contributions

Moghaddas, R., Caraffini, F., & Seisenberger, M. (2025). A Multi-Agent System for Optimal Train Scheduling in Single-Track Railways. In Applications of Evolutionary Computation: 28th European Conference, EvoApplications 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings, Part II (pp. 320-333). Springer.
https://doi.org/10.1007/978-3-031-90065-5_20, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa68687
Caraffini, F., Eshkiki, H., Mohammadpour, M., Sullo, N., & George, C. (2024). Towards Improving Single-Cell Segmentation in Heterogeneous Configurations of Cardiomyocyte Networks. In Lecture Notes in Computer Science (pp. 104-117). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_8, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67383
Enobun, A., Anakwenze, U., Taherkhani, A., Anastassi, Z., Caraffini, F., & Eshkiki, H. (2024). Segmenting Breast Ultrasound Scans Using a Generative Adversarial Network Embedding U-Net. In Lecture Notes in Computer Science (pp. 149-159). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-67285-9_11, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67384
Custode, L., Caraffini, F., Yaman, A., & Iacca, G. (2024). An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1838-1845). ACM.
https://doi.org/10.1145/3638530.3664163, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa67311
Yen, T., Vengusamy, S., Caraffini, F., Kuhn, S., & Colreavy-Donnelly, S. (2023). Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables. In 2023 34th Conference of Open Innovations Association (FRUCT) (pp. 183-191). IEEE.
https://doi.org/10.23919/fruct60429.2023.10328158, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65391
Ader, L., Crowley, K., Kuhn, S., Caraffini, F., Altındağ, T., & Colreavy-Donnelly, S. (2023). Extended Reality, Augmented Users, and Design Implications for Virtual Learning Environments. In 2023 IEEE International Symposium on Technology and Society (ISTAS)IEEE.
https://doi.org/10.1109/istas57930.2023.10305947, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65824
Pérez-Pérez, J., Cruz, I., Sánchez-Pinzón, M., Caraffini, F., & Lochmuller, C. (2023). Forecasting Climate Transition Regulatory and Market Risk Variables with Machine Learning. In 2023 IEEE International Symposium on Technology and Society (ISTAS)IEEE.
https://doi.org/10.1109/istas57930.2023.10306196, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65823
Stein, B., Vermetten, D., Caraffini, F., & Kononova, A. (2023). Deep BIAS: Detecting Structural Bias using Explainable AI. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationACM.
https://doi.org/10.1145/3583133.3590551, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63992
http://dx.doi.org/10.1145/3583133.3590551
Mitran, M., Kononova, A., Caraffini, F., & Zaharie, D. (2023). Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking Suite. In Proceedings of the Companion Conference on Genetic and Evolutionary ComputationACM.
https://doi.org/10.1145/3583133.3596410, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63991
http://dx.doi.org/10.1145/3583133.3596410
Vermetten, D., Caraffini, F., Kononova, A., & Bäck, T. (2023). Modular Differential Evolution. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 864-872). ACM.
https://doi.org/10.1145/3583131.3590417, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63892
http://dx.doi.org/10.1145/3583131.3590417
Muhammad, M., Caraffini, F., Fasanmade, A., Ishola, O., Mohammed, K., & Morden, J. (2023). Data-Driven Design for Anomaly Detection in Network Access Control Systems. In 2023 International Conference on Business Analytics for Technology and Security (ICBATS) (pp. 1-10). IEEE.
https://doi.org/10.1109/icbats57792.2023.10111130, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62224
Hasshu, S., O’Connor, S., Colreavy-Donnelly, S., Kuhn, S., & Caraffini, F. (2022). EmotiMask: Mapping Mouth Movements to an LED Matrix for Improving Recognition When Teaching With a Face Mask. In ECE Official Conference ProceedingsThe International Academic Forum(IAFOR).
https://doi.org/10.22492/issn.2188-1162.2022.49, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61352
Vermetten, D., Caraffini, F., Stein, B., & Kononova, A. (2022). Using structural bias to analyse the behaviour of modular CMA-ES. In Proceedings of the Genetic and Evolutionary Computation Conference CompanionACM.
https://doi.org/10.1145/3520304.3534035, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62444
Vermetten, D., Kononova, A., Caraffini, F., Wang, H., & Bäck, T. (2021). Is there anisotropy in structural bias?. In Proceedings of the Genetic and Evolutionary Computation Conference CompanionACM.
https://doi.org/10.1145/3449726.3463218, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62442
Stein, B., Caraffini, F., & Kononova, A. (2021). Emergence of structural bias in differential evolution. In Proceedings of the Genetic and Evolutionary Computation Conference CompanionACM.
https://doi.org/10.1145/3449726.3463223, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62443
Colreavy-Donnelly, S., Kuhn, S., Caraffini, F., OConnor, S., Anastassi, Z., & Coupland, S. (2020). A Neural Network for Interpolating Light-Sources. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)IEEE.
https://doi.org/10.1109/compsac48688.2020.00-21, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60954
Bonet, I., Caraffini, F., Pena, A., Puerta, A., & Gongora, M. (2020). Oil Palm Detection via Deep Transfer Learning. In 2020 IEEE Congress on Evolutionary Computation (CEC)IEEE.
https://doi.org/10.1109/cec48606.2020.9185838, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60947
Bielby, J., Kuhn, S., Colreavy-Donnelly, S., Caraffini, F., O'Connor, S., & Anastassi, Z. (2020). Identifying Parkinson’s Disease Through the Classification of Audio Recording Data. In 2020 IEEE Congress on Evolutionary Computation (CEC)IEEE.
https://doi.org/10.1109/cec48606.2020.9185915, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60948
Florez-Lozano, J., Caraffini, F., Parra, C., & Gongora, M. (2020). Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform. In 2020 IEEE Congress on Evolutionary Computation (CEC)IEEE.
https://doi.org/10.1109/cec48606.2020.9185686, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60942
Kononova, A., Caraffini, F., Wang, H., & Back, T. (2020). Can Single Solution Optimisation Methods Be Structurally Biased?. In 2020 IEEE Congress on Evolutionary Computation (CEC)IEEE.
https://doi.org/10.1109/cec48606.2020.9185494, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60950
Fox, M., Yang, S., & Caraffini, F. (2020). An Experimental Study of Prediction Methods in Robust optimization Over Time. In 2020 IEEE Congress on Evolutionary Computation (CEC)IEEE.
https://doi.org/10.1109/cec48606.2020.9185910, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60952
Fahy, C., Caraffini, F., & Gongora, M. (2020). A Multi-Agent System for Modelling the Spread of Lethal Wilt in Oil-Palm Plantations. In 2020 IEEE Congress on Evolutionary Computation (CEC)IEEE.
https://doi.org/10.1109/cec48606.2020.9185742, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60955
Kononova, A., Caraffini, F., Wang, H., & Bäck, T. (2020). Can Compact Optimisation Algorithms Be Structurally Biased?. In Parallel Problem Solving from Nature – PPSN XVI (pp. 229-242). Springer International Publishing.
https://doi.org/10.1007/978-3-030-58112-1_16, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60951
Pena, A., Bonet, I., Manzur, D., Gongora, M., & Caraffini, F. (2019). Validation of convolutional layers in deep learning models to identify patterns in multispectral images. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI)IEEE.
https://doi.org/10.23919/cisti.2019.8760741, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60935
Caraffini, F. & Kononova, A. (2019). Structural bias in differential evolution: A preliminary study. In AIP Conference ProceedingsAmerican Institute of Physics.
https://doi.org/10.1063/1.5089972, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60937
Caraffini, F. & Neri, F. (2018). Rotation Invariance and Rotated Problems: An Experimental Study on Differential Evolution. In Applications of Evolutionary Computation (pp. 597-614). Springer International Publishing.
https://doi.org/10.1007/978-3-319-77538-8_41, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60933
Caraffini, F., Neri, F., & Iacca, G. (2017). Large Scale Problems in Practice: The Effect of Dimensionality on the Interaction Among Variables. In Applications of Evolutionary Computation (pp. 636-652). Springer International Publishing.
https://doi.org/10.1007/978-3-319-55849-3_41, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60932
Iacca, G., Caraffini, F., & Neri, F. (2016). Continuous Parameter Pools in Ensemble Differential Evolution. In 2015 IEEE Symposium Series on Computational IntelligenceIEEE.
https://doi.org/10.1109/ssci.2015.216, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60929
Epitropakis, M., Caraffini, F., Neri, F., & Burke, E. (2015). A Separability Prototype for Automatic Memes with Adaptive Operator Selection. In 2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)IEEE.
https://doi.org/10.1109/foci.2014.7007809, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60931
Iacca, G., Neri, F., Caraffini, F., & Suganthan, P. (2014). A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms. In Applications of Evolutionary Computation (pp. 615-626). Springer Berlin Heidelberg.
https://doi.org/10.1007/978-3-662-45523-4_50, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60926
Caraffini, F., Neri, F., Gongora, M., & Passow, B. (2013). Re-sampling search: A seriously simple memetic approach with a high performance. In 2013 IEEE Workshop on Memetic Computing (MC)IEEE.
https://doi.org/10.1109/mc.2013.6608207, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60917
Caraffini, F., Neri, F., & Poikolainen, I. (2013). Micro-differential evolution with extra moves along the axes. In 2013 IEEE Symposium on Differential Evolution (SDE)IEEE.
https://doi.org/10.1109/sde.2013.6601441, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60920
Caraffini, F., Neri, F., Cheng, J., Zhang, G., Picinali, L., Iacca, G., & Mininno, E. (2013). Super-fit Multicriteria Adaptive Differential Evolution. In 2013 IEEE Congress on Evolutionary ComputationIEEE.
https://doi.org/10.1109/cec.2013.6557763, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60915
Iacca, G., Caraffini, F., Neri, F., & Mininno, E. (2013). Single particle algorithms for continuous optimization. In 2013 IEEE Congress on Evolutionary ComputationIEEE.
https://doi.org/10.1109/cec.2013.6557754, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60916
Caraffini, F., Iacca, G., Neri, F., Picinali, L., & Mininno, E. (2013). A CMA-ES super-fit scheme for the re-sampled inheritance search. In 2013 IEEE Congress on Evolutionary ComputationIEEE.
https://doi.org/10.1109/cec.2013.6557692, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60923
Caraffini, F., Iacca, G., Neri, F., & Mininno, E. (2012). Three variants of three Stage Optimal Memetic Exploration for handling non-separable fitness landscapes. In 2012 12th UK Workshop on Computational Intelligence (UKCI)IEEE.
https://doi.org/10.1109/ukci.2012.6335767, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60909
Caraffini, F., Iacca, G., Neri, F., & Mininno, E. (2012). The importance of being structured: A comparative study on multi stage memetic approaches. In 2012 12th UK Workshop on Computational Intelligence (UKCI)IEEE.
https://doi.org/10.1109/ukci.2012.6335768, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60910
Neri, F., Weber, M., Caraffini, F., & Poikolainen, I. (2012). Meta-Lamarckian learning in three stage optimal memetic exploration. In 2012 12th UK Workshop on Computational Intelligence (UKCI)IEEE.
https://doi.org/10.1109/ukci.2012.6335770, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60912
Poikolainen, I., Caraffini, F., Neri, F., & Weber, M. (2012). Handling non-separability in Three Stage Optimal Memetic Exploration. In Proceedings of the Fifth International Conference on Bioinspired Optimization Methods and their Applications
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60913
Iacca, G., Caraffini, F., Neri, F., & Mininno, E. (2012). Robot Base Disturbance Optimization with Compact Differential Evolution Light. In Applications of Evolutionary Computation (pp. 285-294). Springer Berlin Heidelberg.
https://doi.org/10.1007/978-3-642-29178-4_29, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60911

Other Research Outputs

Milani, A., Santucci, V., & Caraffini, F. (2022). Special Issue “Emerging Artificial Intelligence (AI) Technologies for Learning”
https://doi.org/10.3390/app12178819, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61006

Supervision

Enhancing Cardiomyocyte Dynamic Network Analysis with Machine Learning (current)

PhD

Other supervisor: Dr Hassan Eshkiki
Modelling and Optimising Multi-Modal Customer Journeys in Commercial Banking: A Human-Centred Approach to Behavioural Analytics and Outcome Prediction Across Digital, Physical, and Telephony Channels (current)

PhD

Other supervisor: Dr Sean Walton

Other supervisor: Prof Simon Robinson

Other supervisor: Prof Matt Jones
Advancing AI-Driven Techniques for Complex Problem Solving: A Focus on Distributed Constraint Satisfaction Problems (current)

PhD

Other supervisor: Dr Monika Seisenberger
Evolutionary Algorithms and Neural Networks in Personalised Diet Planning: A General Solution (current)

PhD

Other supervisor: Dr Hassan Eshkiki
Enhancing Reading Proficiency: The Impact of AI-Generated Resources on Students Below Expected Reading Age. (current)

MSc

Other supervisor: Prof Faron Moller

Taught Modules

CS-210 Concurrency

This module provides an introduction to the issues raised in developing and using concurrent and distributed systems. Consideration of practical and formal solutions to example problems from operating systems and networking.
CS-235 Software Engineering 2

Building on the programming knowledge and experience acquired by the students in the co - and pre-requisites, this module enables students to apply advanced software development methodologies, with a focus on object-oriented software design and engineering. The module provides students with valuable opportunities to acquire knowledge and experience in important aspects of software engineering with a strong emphasis on teamwork and practical assignments. A significant element of the module is the construction of a substantial software product, where students are assessed on their effectiveness at working together as a team.

Affiliated Positions

Honorary Senior Research Fellow, De Montfort University Leicester

2022 - 2026

Invited Presentations, Lectures and Conferences

Metaheuristic Optimisation: from theory to tool implementation

November 2018

EIA University (Colombia)
Optimisation Applications in Engineering

November 2018

EIA University (Colombia)
Heuristics for Continuous Optimisation – Design, Replicability & Behaviour

June 2025

Norcia, Italy (www.unistrapg.it/en/studiare-in-un-ateneo-internazionale/summer-school/dataslo)

Dr Fabio Caraffini

Email address

Twitter Account

LinkedIn Account

Research Links

About

Areas Of Expertise

Career Highlights

Research Highlights

All Research

Enhancing Cardiomyocyte Dynamic Network Analysis with Machine Learning (current)

Modelling and Optimising Multi-Modal Customer Journeys in Commercial Banking: A Human-Centred Approach to Behavioural Analytics and Outcome Prediction Across Digital, Physical, and Telephony Channels (current)

Advancing AI-Driven Techniques for Complex Problem Solving: A Focus on Distributed Constraint Satisfaction Problems (current)

Evolutionary Algorithms and Neural Networks in Personalised Diet Planning: A General Solution (current)

Enhancing Reading Proficiency: The Impact of AI-Generated Resources on Students Below Expected Reading Age. (current)

CS-210 Concurrency

CS-235 Software Engineering 2

Honorary Senior Research Fellow, De Montfort University Leicester

Metaheuristic Optimisation: from theory to tool implementation

Optimisation Applications in Engineering

Heuristics for Continuous Optimisation – Design, Replicability & Behaviour