Dr Fabio Caraffini

Athro Cyswllt

Computer Science

fabio.caraffini@abertawe.ac.uk

317
Trydydd Llawr
Y Ffowndri Gyfrifiadol
Campws y Bae

Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig

Dolenni Ymchwil

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

Trosolwg

Mae ymchwil Dr Fabio Caraffini yn cynnwys deallusrwydd cyfrifiadol sylfaenol a chymhwysol gyda phwyslais cryf ar hewristeg ar gyfer optimeiddio.
Mae ganddo PhD mewn Technoleg Gwybodaeth Fathemategol (Prifysgol Jyväskylä, y Ffindir, 2016) a Chyfrifiadureg (Prifysgol De Montfort, DU, 2014) a derbyniodd BSc mewn Peirianneg Drydanol ac MSc mewn Peirianneg Delegyfathrebu o Brifysgol Perugia (yr Eidal) yn 2008 a 2011.

Meysydd Arbenigedd

Cyfrifiadura Esblygol
Algorithm Memetig Optimeiddio Hewristaidd a Hyperhewristaidd
Esblygiad Gwahaniaethol
Optimeiddio Heidio Gronynnau
Ymddygiad Algorithmau
Deallusrwydd Cyfrifiadol

Uchafbwyntiau Gyrfa

Diddordebau Addysgu

Rydw i'n Gymrawd yr Academi Addysg Uwch (FHEA)
Cyn ymuno â'r Adran Gyfrifiadureg ym Mhrifysgol Abertawe, dyluniais ac addysgais fodiwlau lefel 4 megis 'Calcwlws I' a 'Dadansoddi Mathemategol' yn ogystal â modiwlau lefel 7 megis
'Optimeiddio Deallusrwydd Cyfrifiadol'

Ymchwil Prif Wobrau

Cyhoeddiadau

Uchafbwyntiau Ymchwil

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 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

Holl Ymchwil

Erthyglau Cylchgronau

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
http://dx.doi.org/10.3390/math11051209
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

Penodau Llyfrau

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

Cyfraniadau Cynadleddau

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 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
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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)IEEE.
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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).
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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.
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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.
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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.
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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.
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Iacca, G., Caraffini, F., & Neri, F. (2016). Continuous Parameter Pools in Ensemble Differential Evolution. In 2015 IEEE Symposium Series on Computational IntelligenceIEEE.
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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.
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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.
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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.
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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.
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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.
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Iacca, G., Caraffini, F., Neri, F., & Mininno, E. (2013). Single particle algorithms for continuous optimization. In 2013 IEEE Congress on Evolutionary ComputationIEEE.
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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.
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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.
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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.
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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
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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.
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Cynnyrch Ymchwil Arall

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

Goruchwyliaeth

Untitled (cyfredol)

PhD

Goruchwyliwr arall: Dr Hassan Eshkiki
Untitled (cyfredol)

PhD

Goruchwyliwr arall: Prof Simon Robinson

Goruchwyliwr arall: Prof Matt Jones

Goruchwyliwr arall: Prof Xianghua Xie
Advancing AI-Driven Techniques for Complex Problem Solving: A Focus on Distributed Constraint Satisfaction Problems (cyfredol)

PhD

Goruchwyliwr arall: Dr Monika Seisenberger
Mapping cellular interactivity in networks of human cardiac data- a machine learning approach«br /»«br /»«br /»«br /»«br /»«br /» (cyfredol)

PhD

Goruchwyliwr arall: Prof Christopher George
Evolutionary Algorithms and Neural Networks in Personalised Diet Planning: A General Solution (cyfredol)

PhD

Goruchwyliwr arall: Dr Hassan Eshkiki

Modiwlau a Addysgir

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.

Swyddi Cysylltiedig

Uwch-gymrawd Ymchwil Er Anrhydedd , Prifysgol De Montfort, Caerlŷr

2022 - 2024

Cyflwyniadau, Darlithoedd a Chynadleddau a wahoddwyd

Metaheuristic Optimisation: from theory to tool implementation

Tachwedd 2018

Prifysgol EIA (Colombia)
Optimisation Applications in Engineering

Tachwedd 2018

Prifysgol EIA (Colombia)

Dr Fabio Caraffini

Cyfeiriad ebost

Dolenni Ymchwil

Trosolwg

Meysydd Arbenigedd

Uchafbwyntiau Gyrfa

Uchafbwyntiau Ymchwil

Holl Ymchwil

Untitled (cyfredol)

Untitled (cyfredol)

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

Mapping cellular interactivity in networks of human cardiac data- a machine learning approach«br /»«br /»«br /»«br /»«br /»«br /» (cyfredol)

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

CS-210 Concurrency

CS-235 Software Engineering 2

Uwch-gymrawd Ymchwil Er Anrhydedd , Prifysgol De Montfort, Caerlŷr

Metaheuristic Optimisation: from theory to tool implementation

Optimisation Applications in Engineering