Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192, Timmis, J. and Nicosia, G. (2022) Assessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies. Swarm and Evolutionary Computation, 74. 101130. ISSN 2210-6502 doi: 10.1016/j.swevo.2022.101130

Amaradio, M. N., Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192, Jansen, G., Gulisano, M., Costanza, J. and Nicosia, G. (2022) Pareto optimal metabolic engineering for the growth-coupled overproduction of sustainable chemicals. Biotechnology and Bioengineering, 119 (7). pp. 1890-1902. ISSN 1097-0290 doi: 10.1002/bit.28103

Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 and Nicosia, G. (2022) Backpropagation neural tree. Neural Networks, 149. ISSN 0893-6080 doi: 10.1016/j.neunet.2022.02.003

Assaf, O., Di Fatta, G. and Nicosia, G. (2022) Multivariate LSTM for stock market volatility prediction. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Jansen, G., Pardalos, P. M., Giuffrida, G. and Umeton, R. (eds.) Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers. Lecture Notes in Computer Science, II (13164). Springer, pp. 531-544. ISBN 978303954697 doi: 10.1007/978-3-030-95470-3_40

Pravin, C., Martino, I., Nicosia, G. and Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 (2021) Adversarial robustness in deep learning: attacks on fragile neurons. In: 30th International Conference on Artificial Neural Networks, September 14-17, 2021, Bratislava, Slovakia (Online), pp. 16-28. doi: 10.1007/978-3-030-86362-3_2

Taylor, R., Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192, Martino, I. and Nicosia, G. (2021) Sensitivity analysis for deep learning: ranking hyper-parameter influence. In: 33rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI2021), 1-3 NOV 2021, Online. doi: 10.1109/ICTAI52525.2021.00083

Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 and Nicosia, G. (2020) Multi-objective optimisation of multi-output neural trees. In: IEEE Congress on Evolutionary Computation (IEEE CEC 2020), 19-24 July 2020, Glasgow, Scotland, UK. doi: 10.1109/CEC48606.2020.9185600

Salvatore D., R., Deyan, D., Giorgio, J., Di Fatta, G. and Nicosia, G. (2020) Pareto multi-task deep learning. In: The 29th International Conference on Artificial Neural Networks (ICANN 2020), 15-18 September 2020, pp. 132-141. doi: 10.1007/978-3-030-61616-8 (Part II)

Patané, A., Jansen, G., Conca, P., Carapezza, G., Costanza, J. and Nicosia, G. (2019) Multi-objective optimization of genome-scale metabolic models: the case of ethanol production. Annals of Operations Research, 276 (1-2). pp. 211-227. ISSN 0254-5330 doi: 10.1007/s10479-018-2865-4

Patanè, A., Santoro, A., Romano, V., Magna, A. L. and Nicosia, G. (2018) Enhancing quantum efficiency of thin-film silicon solar cells by Pareto optimality. Journal of Global Optimization, 72 (3). pp. 491-515. ISSN 0925-5001 doi: 10.1007/s10898-018-0639-9

Santoro, A., Latora, V., Nicosia, G. and Nicosia, V. (2018) Pareto optimality in multilayer network growth. Physical Review Letters, 121 (12). 128302. ISSN 0031-9007 doi: 10.1103/PhysRevLett.121.128302