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Biografia

Mauro Castelli is an associate professor (with aggregation) and a senior research scientist in the field of Computational Intelligence and Machine Learning in the NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa (Portugal). He received his Ph.D. in Computer Science at the University of Milano Bicocca (Italy). His research interests are focused on the study of machine learning methods that can be used to take advantage of the vast amount of data that are produced nowadays. In particular, the research focuses on the development, implementation, and application of computational intelligence systems for addressing complex real-world problems in different domains.
Dr. Castelli has a proven record of outstanding research, as evidenced by existing high-quality publications. He has published his research in a variety of top-quality academic outlets, such as Expert Systems With Applications, IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, Applied Soft Computing, Swarm and Evolutionary Computation, Information Systems Frontiers, among others. Mauro has published more than 120 contributions in the area of machine learning and big data in international conferences and journals.
His pedagogical work includes lectures, seminars, and organization of activities at all levels of programs at the home institution and abroad (Italy, Slovenia, Hungary, Japan). He was responsible for lecturing courses on Big Data technologies, Deep Learning, Computational Intelligence, and many others. He is also the local coordinator of two Erasmus+ Strategic Partnership involving five European universities and focused on the topic of digital transformation and big data technologies.
Dr. Castelli has proved the ability to transfer theoretical insights into practical implications, as demonstrated by his participation in projects with ministers and public administrations including the Directorate General for Education and the Directorate General for Economic Activities among the others. He supervised more than 70 master students and two PhD students. He participated in nine research projects as PI or responsible for several workpackages.

Publicações Cientificas

Akinyelu, A. A., Zaccagna, F., Grist, J. T., Castelli, M., & Rundo, L. (2022)

Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey. Journal of Imaging, 8(8), 1-40. [205]. https://doi.org/10.3390/jimaging8080205

Albuquerque, C., Henriques, R., & Castelli, M. (2022)

A stacking-based artificial intelligence framework for an effective detection and localization of colon polyps. Scientific Reports, 12, 1-12. [17678]. https://doi.org/10.21203/rs.3.rs-1862362/v1, https://doi.org/10.1038/s41598-022-21574-w

Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2022)

Structural similarity index (SSIM) revisited: A data-driven approach. Expert Systems with Applications, 189, 1-19. [116087]. [Advanced online publication on 27 October 2021]. https://doi.org/10.1016/j.eswa.2021.116087

Bakurov, I., Castelli, M., Fontanella, F., Scotto Di Freca, A., & Vanneschi, L. (2022)

A novel binary classification approach based on geometric semantic genetic programming. Swarm and Evolutionary Computation, 69(March), 1-12. [101028]. https://doi.org/10.1016/j.swevo.2021.101028

Castelli, M. (2022)

Special Issue: Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics. [Editorial]. Applied Sciences, 12(15), 1-2. [7924]. https://doi.org/10.3390/app12157924

Castelli, M. (Guest ed.), & Manzoni, L. (Guest ed.) (2022)

Special Issue: Generative Models in Artificial Intelligence and Their Applications (Editorial). Applied Sciences (Switzerland), 12(9), [4127]. https://doi.org/10.3390/app12094127

Castelli, M., Manzoni, L., Mariot, L., Menara, G., & Pietropolli, G. (2022)

The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming. Applied Sciences (Switzerland), 12(10), 1-13. https://doi.org/10.3390/app12104836

Castelli, M., Manzoni, L., Mariot, L., Nobile, M. S., & Tangherloni, A. (2022)

Salp Swarm Optimization: A critical review. Expert Systems with Applications, 189, 1-12. [116029]. [Advanced online publication on 16 October 2021]. Doi: https://doi.org/10.1016/j.eswa.2021.116029.

Costa-mendes, R., Cruz-jesus, F., Oliveira, T., & Castelli, M. (2022)

Deep Learning in Predicting High School Grades: A Quantum Space of Representation. Emerging Science Journal, 6, 166-187. https://doi.org/10.28991/ESJ-2022-SIED-012

Di Noia, C., Grist, J. T., Riemer, F., Lyasheva, M., Fabozzi, M., Castelli, M., Lodi, R., Tonon, C., Rundo, L., & Zaccagna, F. (2022)

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics, 12(9), 1-16. [2125]. https://doi.org/10.3390/diagnostics12092125

Guerra, P., Castelli, M., & Côrte-Real, N. (2022)

Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System. Risks, 10(4), 1-23. [71]. https://doi.org/10.3390/risks10040071

Guerra, P., Castelli, M., & Côrte-real, N. (2022)

Machine learning for liquidity risk modelling: A supervisory perspective. Economic Analysis and Policy, 74(June), 175-187. https://doi.org/10.1016/j.eap.2022.02.001

Henriques, R., Ferreira, A., & Castelli, M. (2022)

A Use Case of Patent Classification Using Deep Learning with Transfer Learning. Journal of Data and Information Science, 7(3), 49-70. https://doi.org/10.2478/jdis-2022-0015

Kandel, I., Castelli, M., & Manzoni, L. (2022)

Brightness as an Augmentation Technique for Image Classification. Emerging Science Journal, 6(4), 881-892. https://doi.org/10.28991/ESJ-2022-06-04-015

Nunes, C., Beatriz-Afonso, A., Cruz-jesus, F., Oliveira, T., & Castelli, M. (2022)

Mathematics and Mother Tongue Academic Achievement: A Machine Learning Approach. Emerging Science Journal, 6(Special Issue: Current Issues, Trends, and New Ideas in Education), 137-149. https://doi.org/10.28991/ESJ-2022-SIED-010

Nunes, C., Oliveira, T., Santini, F. D. O., Castelli, M., & Cruz-jesus, F. (2022)

A Weight and Meta-Analysis on the Academic Achievement of High School Students. Education Sciences, 12(5), 1-17. [287]. https://doi.org/10.3390/educsci12050287

Trujillo, L., Muñoz Contreras, J. M., Hernandez, D. E., Castelli, M., & Tapia, J. J. (2022)

GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming. SoftwareX, 18, 1-7. [101085]. https://doi.org/10.1016/j.softx.2022.101085

Bakurov, I., Buzzelli, M., Castelli, M., Schettini, R., & Vanneschi, L. (2022)

Genetic programming for structural similarity design at multiple spatial scales. In GECCO ’22. Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 911-919). (GECCO 2022 - The Genetic and Evolutionary Computation Conference, July 9-13, Boston, USA). Association for Computing Machinery (ACM). ISBN 978-1-4503-9237-2/22/07

Costa-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2022)

Academic achievement critical factors and the bias and variance decomposition: evidence from high school students’ grades. In Papers of 6th  Canadian International Conference on Advances in Education, Teaching & Technology 2022: Papers proceedings (pp. 54-62). (International Multidisciplinary Research Journal; Vol. Special Issue, No. Conferences - Proceedings). Unique Conferences Canada. https://imrjournal.info/2022/EduTeach2022Proceedings1.pdf

Ferreira, M. et al. (2022)

Fighting Over-Indebtedness: An Artificial Intelligence Approach: An Abstract. In: Pantoja, F., Wu, S. (eds) From Micro to Macro: Dealing with Uncertainties in the Global Marketplace. AMSAC 2020. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham. https://doi.org/10.1007/978-3-030-89883-0_158

Madureira, L., Popovic, A., & Castelli, M. (2021)

Competitive Intelligence Empirical Construct Validation Using Expert In-Depth Interviews Study. In 2021 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) (pp. 1-6) (ICTMOD 2021. IEEE International Conference on Technology Management, Operations and Decisions, 24-26 Nov. 2021, Marrakech, Morocco). IEEE. https://doi.org/10.1109/ICTMOD52902.2021.9739422

Pietropolli, G., Manzoni, L., Paoletti, A., & Castelli, M. (2022)

Combining Geometric Semantic GP with Gradient-Descent Optimization. In E. Medvet, G. Pappa, & B. Xue (Eds.), Genetic Programming. EuroGP 2022: 25th  European Conference, EuroGP 2022 Held as Part of EvoStar 2022 Madrid, Spain, April 20–22, 2022 Proceedings (pp. 19-33). (Lecture Notes in Computer Science; Vol. 13223). Springer. https://doi.org/10.1007/978-3-031-02056-8_2

Albuquerque, C., Vanneschi, L., Henriques, R., Castelli, M., Póvoa, V., Fior, R., & Papanikolaou, N. (2021)

Object detection for automatic cancer cell counting in zebrafish xenografts. PLoS ONE, 16(11), 1-28. [e0260609]. https://doi.org/10.1371/journal.pone.0260609

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021)

General purpose optimization library (Gpol): A flexible and efficient multi-purpose optimization library in python. Applied Sciences (Switzerland), 11(11), 1-34. [4774]. https://doi.org/10.3390/app11114774

Bakurov, I; Castelli, M.; Gau, O; Fontanella, F. & Vanneschi, L. (2021)

Genetic Programming for Stacked Generalization. Swarm and Evolutionary Computation, 100913. [Advanced online publication on 26 may 2021]. https://doi.org/10.1016/j.swevo.2021.100913.

Boto Ferreira, M., Costa Pinto, D., Maurer Herter, M., Soro, J., Vanneschi, L., Castelli, M., & Peres, F. (2021)

Using artificial intelligence to overcome over-indebtedness and fight poverty. Journal of Business Research, 131, 411-425. [Advanced online publication on 19 October 2020]. https://doi.org/10.1016/j.jbusres.2020.10.035

Castelli, M., Manzoni, L., Espindola, T., Popovič, A., & De Lorenzo, A. (2021)

Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. PLoS ONE, 16(11), 1-30. [e0260308]. https://doi.org/10.1371/journal.pone.0260308

Costa-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2021)

Machine learning bias in predicting high school grades: A knowledge perspective. Emerging Science Journal, 5(5), 576-597. https://doi.org/10.28991/esj-2021-01298

Farias, E. C. D., Di Noia, C., Han, C., Sala, E., Castelli, M., & Rundo, L. (2021)

Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features. Scientific Reports, 11(21361), 1-12. [21361]. https://doi.org/10.1038/s41598-021-00898-z

Guerra, P., & Castelli, M. (2021)

Machine learning applied to banking supervision a literature review. Risks, 9(7), 1-24. [136]. https://doi.org/10.3390/risks9070136

Kandel, I., & Castelli, M. (2021)

Improving convolutional neural networks performance for image classification using test time augmentation: a case study using MURA dataset. Health information science and systems, 9(1), 1-22. [33]. https://doi.org/10.1007/s13755-021-00163-7

Kandel, I., Castelli, M., & Popovic, A. (2021)

Comparing stacking ensemble techniques to improve musculoskeletal fracture image classification. Journal of Imaging, 7(6), 1-24. [100]. https://doi.org/10.3390/JIMAGING7060100

Madureira, L., Popovic, A., & Castelli, M. (2021)

Competitive intelligence: A unified view and modular definition. Technological Forecasting and Social Change, 173, 1-17. [121086]. https://doi.org/10.1016/j.techfore.2021.121086

Peres, F., & Castelli, M. (2021)

Combinatorial optimization problems and metaheuristics: Review, challenges, design, and development. Applied Sciences (Switzerland), 11(14), 1-39. [6449]. https://doi.org/10.3390/app11146449

Peres, F., Fallacara, E., Manzoni, L., Castelli, M., Popovic, A., Rodrigues, M., & Estevens, P. (2021)

Time series clustering of online gambling activities for addicted users’ detection. Applied Sciences (Switzerland), 11(5), [2397]. https://doi.org/10.3390/app11052397

Raglio, A., Baiardi, P., Vizzari, G., Imbriani, M., Castelli, M., Manzoni, S., Vico, F., & Manzoni, L. (2021)

Algorithmic music for therapy: Effectiveness and perspectives. Applied Sciences (Switzerland), 11(19), 1-13. [8833]. https://doi.org/10.3390/app11198833

Raglio, A., Castelli, M., Manzoni, L., & Vigo, F. (2021)

What happens if algorithmic music meets medicine. Giornale Italiano di Medicina del Lavoro ed Ergonomia, 43(4), 379-381.

Vanneschi, L., & Castelli, M. (2021)

Soft target and functional complexity reduction: A hybrid regularization method for genetic programming. Expert Systems with Applications, 177, 1-11. [114929]. https://doi.org/10.1016/j.eswa.2021.114929

Benevides, P. J., Silva, N., Costa, H., Moreira, F. D., Moraes, D., Castelli, M., & Caetano, M. (2021)

Land cover mapping at national scale with Sentinel-2 and LUCAS: a case study in Portugal. In C. M. U. Neale, & A. Maltese (Eds.), Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII (Vol. 11856). [1185606] (Proceedings of SPIE). SPIE-International Society for Optical Engineering. https://doi.org/10.1117/12.2598789

Jakobovic, D., Manzoni, L., Mariot, L., Picek, S., & Castelli, M. (2021)

CoInGP: Convolutional inpainting with genetic programming. In GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference (pp. 795-803). (GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3449639.3459346

Abdelaziz, A., Anastasiadou, M., & Castelli, M. (2020)

A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment. Applied Sciences (Switzerland), 10(18), 1-25. [2806]. https://doi.org/10.3390/APP10186538

Besozzi, D., Manzoni, L., Nobile, M. S., Spolaor, S., Castelli, M., Vanneschi, L., ... Tangherloni, A. (2019)

Computational Intelligence for Life Sciences. Fundamenta Informaticae, 171(1-4), 57-80. https://doi.org/10.3233/FI-2020-1872

Castelli, M., Clemente, F. M., Popovic, A., Silva, S., & Vanneschi, L. (2020)

A Machine Learning Approach to Predict Air Quality in California. Complexity, 2020, 1-23. [8049504]. https://doi.org/10.1155/2020/8049504

Castelli, M., Dobreva, M., Henriques, R., & Vanneschi, L. (2020)

Predicting Days on Market to Optimize Real Estate Sales Strategy. Complexity, 2020, 1-22. [4603190]. https://doi.org/10.1155/2020/4603190

Castelli, M., Dondi, R., & Hosseinzadeh, M. M. (2020)

Genetic algorithms for finding episodes in temporal networks. Procedia Computer Science, 176, 215-224. https://doi.org/10.1016/j.procs.2020.08.023

Castelli, M., Groznik, A., & Popovic, A. (2020)

Forecasting electricity prices: A machine learning approach. Algorithms, 13(5), 1-16. [119]. https://doi.org/10.3390/A13050119

Costa-Mendes, R., Oliveira, T., Castelli, M., & Cruz-Jesus, F. (2021)

A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26(2), 1527-1547. https://doi.org/10.1007/s10639-020-10316-y

Cruz-Jesus, F., Castelli, M., Oliveira, T., Mendes, R., Nunes, C., Sa-Velho, M., & Rosa-Louro, A. (2020)

Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon, 6(6), [e04081]. https://doi.org/10.1016/j.heliyon.2020.e04081

De Lorenzo, A., Bartoli, A., Castelli, M., Medvet, E., & Xue, B. (2020)

Genetic programming in the twenty-first century: a bibliometric and content-based analysis from both sides of the fence. Genetic Programming And Evolvable Machines, 21(1-2), 181–204. [Adavanced online publication on 27 July 2019]. doi: https://doi.org/10.1007/s10710-019-09363-3

Kandel, I., & Castelli, M. (2020)

A novel architecture to classify histopathology images using convolutional neural networks. Applied Sciences (Switzerland), 10(8), 1-17. [2929]. https://doi.org/10.3390/APP10082929

Kandel, I., & Castelli, M. (2020)

How deeply to fine-tune a convolutional neural network: A case study using a histopathology dataset. Applied Sciences (Switzerland), 10(10), [3359]. https://doi.org/10.3390/APP10103359

Kandel, I., & Castelli, M. (2020)

The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express. https://doi.org/10.1016/j.icte.2020.04.010

Kandel, I., & Castelli, M. (2020)

Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sciences (Switzerland), 10(6), [2021]. https://doi.org/10.3390/app10062021

Kandel, I., Castelli, M., & Popovic, A. (2020)

Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images. Journal of Imaging, 6(9), 1-17. [0092]. https://doi.org/10.3390/JIMAGING6090092

Kandel, I., Castelli, M., & Popovic, A. (2020)

Musculoskeletal Images Classification for Detection of Fractures Using Transfer Learning. Journal of Imaging, 6(11), 1-14. [127]. https://doi.org/10.3390/jimaging6110127

Lapa, P., Castelli, M., Gonçalves, I., Sala, E., & Rundo, L. (2020)

A hybrid end-to-end approach integrating conditional random fields into CNNs for prostate cancer detection on MRI. Applied Sciences (Switzerland), 10(1), [338]. [Special Issue: Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)]. Doi: https://doi.org/10.3390/app10010338

Manzoni, L., Bartoli, A., Castelli, M., Goncalves, I., & Medvet, E. (2020)

Specializing Context-Free Grammars with a (1 + 1)-EA. IEEE Transactions on Evolutionary Computation, 24(5), 960-973. [9047973]. https://doi.org/10.1109/TEVC.2020.2983664

Raglio, A., Imbriani, M., Imbriani, C., Baiardi, P., Manzoni, S., Gianotti, M., ... Manzoni, L. (2020)

Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial. Computer Methods and Programs in Biomedicine, 185, [105160]. https://doi.org/10.1016/j.cmpb.2019.105160

Tonini, A., Redweik, P., Painho, M., & Castelli, M. (2020)

Remote estimation of target height from unmanned aerial vehicle (Uav) images. Remote Sensing, 12(21), 1-24. [3602]. https://doi.org/10.3390/rs12213602

Gonçalves I., Seca M., Castelli M. (2020)

Bakurov, I., Buzzelli, M., Castelli, M., Schettini, R., & Vanneschi, L. (2020)

Parameters optimization of the Structural Similarity Index. In London Imaging Meeting 2020: Future Colour Imaging (1 ed., Vol. 2020, pp. 19-23). (London Imaging Meeting). https://doi.org/10.2352/issn.2694-118X.2020.LIM-13

Custode, L. L., Tecce, C. L., Bakurov, I., Castelli, M., Cioppa, A. D., & Vanneschi, L. (2020)

A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks. In P. A. Castillo, J. L. Jiménez Laredo, & F. Fernández de Vega (Eds.), Applications of Evolutionary Computation - 23rd  European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings (pp. 513-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12104 LNCS). Springer. https://doi.org/10.1007/978-3-030-43722-0_33

Manzoni, L., Jakobovic, D., Mariot, L., Picek, S., & Castelli, M. (2020)

Towards an evolutionary-based approach for natural language processing. In GECCO 2020: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 985-993). (GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference). Association for Computing Machinery. https://doi.org/10.1145/3377930.3390248

Vanneschi, L., Castelli, M., Manzoni, L., Silva, S., & Trujillo, L. (2020)

Is k Nearest Neighbours Regression Better Than GP? In T. Hu, N. Lourenço, E. Medvet, & F. Divina (Eds.), Genetic Programming - 23rd  European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings (pp. 244-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12101 LNCS). Springer. https://doi.org/10.1007/978-3-030-44094-7_16

Castelli, M., & Manzoni, L. (2019)

GSGP-C++ 2.0: A geometric semantic genetic programming framework. SoftwareX, 10, [100313]. https://doi.org/10.1016/j.softx.2019.100313

Castelli, M., Cattaneo, G., Manzoni, L., & Vanneschi, L. (2019)

A distance between populations for n-points crossover in genetic algorithms. Swarm and Evolutionary Computation, 44(February), 636-645. [Advanced online publication on 21 august 2018. DOI: 10.1016/j.swevo.2018.08.007

Castelli, M., Dondi, R., Mauri, G., & Zoppis, I. (2019)

Comparing incomplete sequences via longest common subsequence. Theoretical Computer Science. [Advanced online publication on 19 september 2019]. Doi:https://doi.org/10.1016/j.tcs.2019.09.022

Hajek, P., Henriques, R., Castelli, M., & Vanneschi, L. (2019)

Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer. Computers and Operations Research, 106(June), 179-190. [advanced online on 7 February 2018]https://doi.org/10.1016/j.cor.2018.02.001 . Doi: https://doi.org/10.1016/j.cor.2018.02.001

Janke, J., Castelli, M., & Popovic, A. (2019)

Analysis of the proficiency of fully connected neural networks in the process of classifying digital images: Benchmark of different classification algorithms on high-level image features from convolutional layers. Expert Systems with Applications, 135, 12-38. https://doi.org/10.1016/j.eswa.2019.05.058

Ruberto, S., Vanneschi, L., & Castelli, M. (2019)

Genetic programming with semantic equivalence classes. Swarm and Evolutionary Computation, 44(February), 453-469. [Advanced online publication at 15 June 2018]. DOI: 10.1016/j.swevo.2018.06.001

Rubio-Largo, A., Vanneschi, L., Castelli, M., & Vega-Rodriguez, M. A. (2019)

Multiobjective Metaheuristic to Design RNA Sequences. IEEE Transactions on Evolutionary Computation, 23(1). DOI: 10.1109/TEVC.2018.2844116

Vanneschi, L., Castelli, M., Scott, K., & Trujillo, L. (2019)

Alignment-based genetic programming for real life applications. Swarm and Evolutionary Computation, 44(February), 840-851. [Advanced online publication on 29 september 2018]. DOI: 10.1016/j.swevo.2018.09.006

Castelli, M., Vanneschi, L., & Largo, Á. R. (2019)

Supervised Learning: Classification. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), Encyclopedia of Bioinformatics and Computational Biology (pp. 342-349). Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20332-4

Vanneschi, L., & Castelli, M. (2019)

Delta Rule and Backpropagation. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), Encyclopedia of Bioinformatics and Computational Biology (Vol. 1, pp. 621-633). Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20340-3

Vanneschi, L., & Castelli, M. (2019)

Multilayer Perceptrons. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), Encyclopedia of Bioinformatics and Computational Biology (pp. 612-620). Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20339-7

Bakurov, I., Castelli, M., Fontanella, F., & Vanneschi, L. (2019)

A regression-like classification system for geometric semantic genetic programming. In J. J. Merelo, J. Garibaldi, A. Linares-Barranco, K. Madani, K. Warwick, & K. Warwick (Eds.), Proceedings of the 11th  International Joint Conference on Computational Intelligence (IJCCI 2019) (Vol. 1, pp. 40-48). (IJCCI 2019 - Proceedings of the 11th  International Joint Conference on Computational Intelligence). SciTePress.

Bakurov, I., Castelli, M., Vanneschi, L., & Freitas, M. J. (2019)

Supporting medical decisions for treating rare diseases through genetic programming. In P. Kaufmann, & P. A. Castillo (Eds.), Applications of Evolutionary Computation: 22nd  International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Proceedings (pp. 187-203). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11454 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-16692-2_13. ISBN: 978-3-030-16691-5; Online ISBN: 978-3-030-16692-2

Castelli, M., Dondi, R., Manzoni, S., Mauri, G., & Zoppis, I. (2019)

Top k 2-clubs in a network: A genetic algorithm. In J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, ... P. M. A. Sloot (Eds.), Computational Science. ICCS 2019: 19th  International Conference, 2019, Proceedings (Vol. 5, pp. 656-663). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11540 LNCS). Springer Verlag.

Castelli, M., Manzoni, L., Mariot, L., & Saletta, M. (2019)

Extending local search in geometric semantic genetic programming. In P. Moura Oliveira, P. Novais, & L. P. Reis (Eds.), Progress in Artificial Intelligence : 19th  EPIA Conference on Artificial Intelligence, EPIA 2019, Proceedings (pp. 775-787). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11804 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-30241-2_64

Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019)

Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th  International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3

Lapa, P., Gonçalves, I., Rundo, L., & Castelli, M. (2019)

Semantic learning machine improves the CNN-based detection of prostate cancer in non-contrast-enhanced MRI. In M. López-Ibáñez (Ed.), GECCO 2019 Companion : Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 1837-1845). New York: Association for Computing Machinery, Inc. ISBN: 978-1-4503-6748-6. https://doi.org/10.1145/3319619.3326864

Lapa, P., Rundo, L., Gonçalves, I., & Castelli, M. (2019)

Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: A study with the semantic learning machine. In GECCO 2019 : Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 381-382). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3322035

Madureira, L., Castelli, M., & Popovic, A. (2019)

Design thinking: the new mindset for competitive intelligence? Impacts on the competitive intelligence model. In Proceedings of the 19th  Portuguese Association of Information Systems Conferemce: digital disruption: living between data science, IoT and ... people (pp. 34). Associação Portuguesa de Sistemas de Informação.

Re, A., Vanneschi, L., & Castelli, M. (2019)

Universal learning machine with genetic programming. In J. J. Merelo, J. Garibaldi, A. Linares-Barranco, K. Madani, K. Warwick, & K. Warwick (Eds.), Proceedings of the 11th  International Joint Conference on Computational Intelligence (Vol. 1, pp. 115-122). (IJCCI 2019 - Proceedings of the 11th  International Joint Conference on Computational Intelligence). Viena: SciTePress.

Agapito, G.; Cannataro, M.; Castelli, M.; Dondi, R.; Santos, R. W dos & Zoppis, I. (Eds.) (2018)

Biomedical and Bioinformatics Challenges for Computer Science [Special Issue]. Computers, 7, 17.

Agapito, G.; Cannataro, M.; Castelli, M.; Dondi, R.; Zoppis, I.(2018)

Editorial of the Special Issue of the 10th  Workshop on Biomedical and Bioinformatics Challenges for Computer Science—BBC 2017, Computers, 7, 17, 1-2. doi:10.3390/computers7010017

Bartoli, A., Castelli, M., & Medvet, E. (2018)

Weighted Hierarchical Grammatical Evolution. IEEE Transactions on Cybernetics. (Advanced online publication on 6 november 2018). DOI: 10.1109/TCYB.2018.2876563

Beretta, S., Castelli, M., Goncalves, I., Henriques, R., & Ramazzotti, D. (2018)

Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes. Complexity, [1591878]. DOI: 10.1155/2018/1591878

Beretta, S., Castelli, M., Gonçalves, I., Kel, I., Giansanti, V., & Merelli, I. (2018)

Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution. Journal of Computational Biology, 25(10), 1091-1105. DOI: 10.1089/cmb.2017.0167

Beretta, S.; Castelli, M.; Munoz, L.; Trujillo, L.; Martínez, Y.; Popovic, A.; Milanesi, L. & Merelli, L. (2018)

A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP. Complexity, Article ID 4963139. doi: https://doi.org/10.1155/2018/4963139.

Cagnoni, S., & Castelli, M. (2018)

[Editorial]. Special issue on computational intelligence and nature-inspired algorithms for real-world data analytics and pattern recognition. Algorithms, 11(3), 1-2. DOI: 10.3390/a11030025

Cagnoni, S., & Castelli, M. (Eds). (2018)

Special issue on computational intelligence and nature-inspired algorithms for real-world data analytics and pattern recognition. Algorithms, 11(3).

Medvet, E., Virgolin, M., Castelli, M., Bosman, P. A. N., Gonçalves, I., & Tušar, T. (2018)

Unveiling evolutionary algorithm representation with DU maps. Genetic Programming And Evolvable Machines, 19 (3), 351–389. DOI: 10.1007/s10710-018-9332-5

Muñoz, L., Trujillo, L., Silva, S., Castelli, M., & Vanneschi, L. (2018)

Evolving multidimensional transformations for symbolic regression with M3GP. Memetic computing. [Advanced online publication on 24 august 2018]DOI: 10.1007/s12293-018-0274-5. URL: https://doi.org/10.1007/s12293-018-0274-5

Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018)

The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20(2), 209-222. (advanced online publication on 24 october 2016). DOI: 10.1007/s10796-016-9720-4

Rubio-Largo, Á., Castelli, M., Vanneschi, L., & Vega-Rodríguez, M. A. (2018)

A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment. Journal of Computational Biology, 25(9), 1009-1022. DOI: 10.1089/cmb.2018.0031

Rubio-Largo, A., Vanneschi, L., Castelli, M., & Vega-Rodriguez, M. A. (2018)

A Characteristic-Based Framework for Multiple Sequence Aligners. IEEE Transactions on Cybernetics (advanced online publication on 2 october 2016). DOI: 10.1109/TCYB.2016.2621129

Rubio-Largo, Á., Vanneschi, L., Castelli, M., & Vega-Rodríguez, M. A. (2018)

Multiobjective characteristic-based framework for very-large multiple sequence alignment. Applied Soft Computing Journal, 69, 719-736. [Advanced online publication on 27 June 2017]. DOI: 10.1016/j.asoc.2017.06.

Rubio-Largo, Á., Vanneschi, L., Castelli, M., & Vega-Rodríguez, M. A. (2018)

Swarm intelligence for optimizing the parameters of multiple sequence aligners. Swarm and Evolutionary Computation. [advanced online publication on 24 april 2018]. DOI: 10.1016/j.swevo.2018.04.003

Vaneschi, L.; Horn, D. M.; Castelli, M.; Popovic, A. (2018)

An Artificial Intelligence System for Predicting Customer Default in E-Commerce. Expert Systems With Applications, 104, 1-21. doi: https://doi.org/10.1016/j.eswa.2018.03.025

Vanneschi, L.; Castelli, M.; Scott, K. & Popovic, A. (2018)

Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming. International Journal of Concrete Structures and Materials System, 12:72. doi: https://doi.org/10.1186/s40069-018-0300-5

Ribeiro, S., Henriques, R. & Castelli, M. (2018)

Modelo de otimização. In T. Rodrigues, & M. Painho (Eds.), Modelos preditivos e segurança pública (pp. 281-302). Porto: Fronteira do Caos. ISBN: 978-989-54148-7-1

Trujillo, L.; Z-Flores, E.; Juárez-Smith, P. S.; Legrand, P.; Silva, S.; Castelli, M.; Vanneschi, L.; Schütze, O. & Muñoz, L. (2018)

Local Search is Underused in Genetic Programming. In R. Riolo et. al. (Eds.), Genetic Programming Theory and Practice XIV, pp. 119-137. [Genetic and Evolutionary Computation]. Springer. ISBN: 978-3-319-97087-5; Online ISBN: 978-3-319-97088-2. Doi: https://doi.org/10.1007/978-3-319-97088-2_8

Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., & García-Sánchez, P. (2018)

Genetic Programming: 21st  European Conference, EuroGP 2018, Proceedings. (Lecture Notes in Computer Science; Vol. 10781). Springer. DOI: 10.1007/978-3-319-77553-1

Bakurov, I., Vanneschi, L., Castelli, M., & Fontanella, F. (2018)

EDDA-V2: an improvement of the evolutionary demes despeciation algorithm. In Parallel Problem Solving from Nature – PPSN XV: 15th  International Conference, 2018, Proceedings (pp. 185-196). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11101 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-99253-2_15

Castelli, M., Gonçalves, I., Manzoni, L., & Vanneschi, L. (2018)

Pruning techniques for mixed ensembles of genetic programming models. In M. Castelli, L. Sekanina, M. Zhang, S. Cagnoni, & P. García-Sánchez (Eds.), Genetic Programming : 21st  European Conference, EuroGP 2018, Proceedings (pp. 52-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10781 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-77553-1_4

Jagusch, J.-B.-; Gonçalves, I & Castelli, M. (2018)

Neuroevolution under unimodal error landscapes: an exploration of the semantic learning machine algorithm. GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 159-160. (Genetic and Evolutionary Computation Conference Companion, GECCO 2018,Kyoto, Japan, July 15 - 19, 2018). New York: ACM. ISBN: 978-1-4503-5764-7. Doi: 10.1145/3205651.3205778

Vanneschi L., Scott K., Castelli M. (2018)

A Multiple Expression Alignment Framework for Genetic Programming. In: Castelli M., Sekanina L., Zhang M., Cagnoni S., García-Sánchez P. (Eds.). Genetic Programming. EuroGP 2018. Proceedings of the 21st  European Conference on Genetic Programming, EuroGP 2018; Parma; Italy; 4 April 2018 through 6 April 2018. Lecture Notes in Computer Science, vol 10781. Springer. ISBN: 978-3-319-77552-4. doi: https://doi.org/10.1007/978-3-319-77553-1_11

Castelli, M., Manzoni, L., Silva, S., Vanneschi, L., & Popovic, A. (2017)

The influence of population size in geometric semantic GP. Swarm and Evolutionary Computation, 32, 110-120. DOI: 10.1016/j.swevo.2016.05.004

Castelli, M., Sormani, R., Trujillo, L., & Popovic, A. (2017)

Predicting per capita violent crimes in urban areas: an artificial intelligence approach. Journal of Ambient Intelligence and Humanized Computing, 8(1), 29-36. DOI: 10.1007/s12652-015-0334-3

Castelli, M., Vanneschi, L., Trujillo, L., & Popovic, A. (2017)

Stock index return forecasting: Semantics-based genetic programming with local search optimiser. International Journal of Bio-Inspired Computation, 10(3), 159-171. DOI: 10.1504/IJBIC.2017.086699

Leonardo Vanneschi, Mauro Castelli & Alessandro Re (2017)

Prediction of ships' position by analysing AIS data: an artificial intelligence approach. International Journal of Web Engineering and Technology, 12(3), 253-274. Doi: 10.1504/IJWET.2017.088389

Leonardo Vanneschi; Roberto Henriques; Mauro Castelli (2017)

Multi-objective genetic algorithm with variable neighbourhood search for the electoral redistricting problem. Swarm and Evolutionary Computation, 36, 37-51. https://doi.org/10.1016/j.swevo.2017.04.003

Mauro Castelli, Ivo Gonçalves, Leonardo Trujillo Aleš Popovic (2017)

An evolutionary system for ozone concentration forecasting, Information Systems Frontiers, 19 (5), 1123–1132. https://doi.org/10.1007/s10796-016-9706-2

Mauro Castelli, Leonardo Trujillo, Ivo Gonçalves and Ales Popovic (2017)

An evolutionary system for the prediction of high performance concrete strength. Computers and concrete: an international journal, 19(6), 651-658. DOI: 10.12989/cac.2017.19.6.651

Mauro Castelli, Luca Manzoni, Leonardo Vanneschia, Aleš Popovič (2017)

An Expert System for Extracting Knowledge from Customers' Reviews: The Case of Amazon.com, Inc. Expert Systems with Applications, 84, 117-126. https://doi.org/10.1016/j.eswa.2017.05.008

Rubio-Largo, A., Vanneschi, L., Castelli, M. & Vega-Rodríguez, M. A. (2017)

Reducing Alignment Time Complexity of Ultra-large Sets of Sequences. Journal of Computational Biology, 24(11): 1144-1154. https://doi.org/10.1089/cmb.2017.0097

Rubio-Largo, Á., Vanneschi, L., Castelli, M., & Vega-Rodríguez, M. A. (2017)

Using biological knowledge for multiple sequence aligner decision making. Information Sciences, 420, 278-298. DOI: 10.1016/j.ins.2017.08.069

McDermott, J.; Castelli, M.; Sekanina, L.; Haasdijk, E. & García-Sánchez, P. (Eds.). (2017)

Genetic Programming. 20th  European Conference, EuroGP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings. (Lecture Notes in Computer Science, 10196). Springer: ISBN: 978-3-319-55695-6, Doi: https://doi.org/10.1007/978-3-319-55696-3

Castelli, M., Dondi, R., Mauri, G., & Zoppis, I. (2017)

The longest filled common subsequence problem. In 28th  Annual Symposium on Combinatorial Pattern Matching, CPM 2017 (Vol. 78). [14] Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. DOI: 10.4230/LIPIcs.CPM.2017.14

Gongalves, I., Fonseca, C. M., Silva, S., & Castelli, M. (2017)

Unsure when to stop? : Ask your semantic neighbors. In GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference (pp. 929-936). Association for Computing Machinery, Inc. DOI: 10.1145/3071178.3071328

Goribar-Jimenez, C., Maldonado, Y., Trujillo, L., Castelli, M., Goncalves, I., & Vanneschi, L. (2017)

Towards the development of a complete GP system on an FPGA using geometric semantic operators. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 1932-1939). [7969537] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/CEC.2017.7969537

Vanneschi, L., Bakurov, I., & Castelli, M. (2017)

An initialization technique for geometric semantic GP based on demes evolution and despeciation. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 113-120). [7969303] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/CEC.2017.7969303

Vanneschi, L., Castelli, M., Goncalves, I., Manzoni, L., & Silva, S. (2017)

Geometric semantic genetic programming for biomedical applications: A state of the art upgrade. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 177-184). [7969311] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/CEC.2017.7969311

Vanneschi, L., Castelli, M., Manzoni, L., Krawiec, K., Moraglio, A., Silva, S., & Gonçalves, I. (2017)

PSXO: population-wide semantic crossover. In GECCO '17 : Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 257-258). Association for Computing Machinery, Inc. DOI: 10.1145/3067695.3076003

Beretta, S., Cannataro, M., & Castelli, M. (2016)

9th  workshop on biomedical and bioinformatics challenges for computer science - BBC2016. [Editorial]. Procedia Computer Science, 80, 962-964. doi: 10.1016/j.procs.2016.05.390

Beretta, S., Castelli, M., & Dondi, R. (2016). Erratum: Corrigendum to “Parameterized tractability of the maximum-duo preservation string mapping problem” (Theoretical Computer Science (2016)

Erratum: Corrigendum to “Parameterized tractability of the maximum-duo preservation string mapping problem” (Theoretical Computer Science (2016) 646 (16–25) (S0304397516303255) (10.1016/j.tcs.2016.07.011)). [Erratum]. Theoretical Computer Science, 653, 108-110. doi: 10.1016/j.tcs.2016.09.015

Beretta, S., Castelli, M., & Dondi, R. (2016)

Parameterized tractability of the maximum-duo preservation string mapping problem. Theoretical Computer Science, 646, 16-25. doi: 10.1016/j.tcs.2016.07.011

Castelli, M., & Fumagalli, A. (2016)

An evolutionary system for exploitation of fractured geothermal reservoirs. [Article]. Computational Geosciences, 20(2), 385-396. doi: 10.1007/s10596-015-9552-1

Castelli, M., Manzoni, L., & Popovic, A. (2016)

An Artificial Intelligence System to Predict Quality of Service in Banking Organizations. Computational Intelligence and Neuroscience. doi: 10.1155/2016/9139380

Castelli, M., Manzoni, L., Vanneschi, L., Silva, S., & Popovic, A. (2016)

Self-tuning geometric semantic Genetic Programming. Genetic Programming and Evolvable Machines, 17(1), 55-74. doi: 10.1007/s10710-015-9251-7

Castelli, M., Trujillo, L., Vanneschi, L., & Popovic, A. (2016)

Prediction of relative position of CT slices using a computational intelligence system. [Article]. Applied Soft Computing, 46, 537-542. doi: 10.1016/j.asoc.2015.09.021

Castelli, M., Vanneschi, L., & Popovic, A. (2016)

Controlling Individuals Growth in Semantic Genetic Programming through Elitist Replacement. Computational Intelligence and Neuroscience, 2016, 12. doi: 10.1155/2016/8326760

Castelli, M., Vanneschi, L., & Popovic, A. (2016)

Parameter evaluation of geometric semantic genetic programming in pharmacokinetics. International Journal of Bio-Inspired Computation, 8(1), 42-50. doi: 10.1504/ijbic.2016.074634

Castelli, M., Vanneschi, L., Manzoni, L., & Popovic, A. (2016)

Semantic genetic programming for fast and accurate data knowledge discovery. Swarm and Evolutionary Computation, 26, 1-7. doi: http://dx.doi.org/10.1016/j.swevo.2015.07.001

Enríquez-Zárate, J., Trujillo, L., de Lara, S., Castelli, M., Z-Flores, E., Muñoz, L., & Popovič, A. (2017)

Automatic modeling of a gas turbine using genetic programming: An experimental study. Applied Soft Computing, 50, 212-222. doi: http://dx.doi.org/10.1016/j.asoc.2016.11.019

Beretta, S., Castelli, M., Martinez, Y., Munoz, L., Silva, S., Trujillo, L., . . . Merelli, I. (2016)

A Machine Learning Approach for the Integration of miRNA-target Predictions. In Y. Cotronis, M. Daneshtalab & G. A. Papadopoulos (Eds.), 2016 24th  Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (pp. 528-534). New York: Ieee.

Re, A., Castelli, M., & Vanneschi, L. (2016)

A Comparison Between Representations for Evolving Images. In C. Johnson, V. Ciesielski, J. Correia & P. Machado (Eds.), Evolutionary and Biologically Inspired Music, Sound, Art and Design: 5th  International Conference, EvoMUSART 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings (pp. 163-185). Cham: Springer International Publishing.

Heywood, M. I., McDermott, J., Castelli, M., Costa, E., & Sim, K. (Eds.). (2016)

Genetic programming: 19th  European conference, EuroGP 2016 Porto, Portugal, march 30 – April 1, 2016 proceedings. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9594). Springer-Verlag. DOI: 10.1007/978-3-319-30668-1

Castelli, M., Manzoni, L., Gonçalves, I., Vanneschi, L., Trujillo, L., & Silva, S. (2016)

An analysis of geometric semantic crossover: A computational geometry approach. In ECTA 2016 - 8th  International Conference on Evolutionary Computation Theory and Applications (Vol. 1, pp. 201-208). SciTePress. DOI: 10.5220/0006056402010208

Goncalves, I., Silva, S., Fonseca, C. M., & Castelli, M. (2016)

Arbitrarily Close Alignments in the Error Space: a Geometric Semantic Genetic Programming Approach. Paper presented at the Proceedings of the 2016 Genetic and Evolutionary Computation Conference (Gecco'16 Companion).

Beretta, S., Castelli, M., & Dondi, R. (2015)

Correcting gene tree by removal and modification: Tractability and approximability. Journal of Discrete Algorithms, 33, 115-129. doi: http://dx.doi.org/10.1016/j.jda.2015.03.005

Castelli, M., Henriques, R., & Vanneschi, L. (2015)

A geometric semantic genetic programming system for the electoral redistricting problem. Neurocomputing, 154, 200-207. doi: 10.1016/j.neucom.2014.12.003

Castelli, M., Silva, S., & Vanneschi, L. (2015)

A C ++ framework for geometric semantic genetic programming. Genetic Programming and Evolvable Machines, 16(1), 73-81. doi: 10.1007/s10710-014-9218-0

Castelli, M., Trujillo, L., & Vanneschi, L. (2015)

Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer. Computational Intelligence and Neuroscience, 2015, 8 pp. doi: 10.1155/2015/971908

Castelli, M., Trujillo, L., Vanneschi, L., & Popovic, A. (2015)

Prediction of energy performance of residential buildings: A genetic programming approach. Energy and Buildings, 102, 67-74. doi: 10.1016/j.enbuild.2015.05.013

Castelli, M., Vanneschi, L., & De Felice, M. (2015)

Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case. Energy Economics, 47, 37-41. doi: 10.1016/j.eneco.2014.10.009

Castelli, M., Vanneschi, L., & Popovic, A. (2015)

Predicting burned areas of forestry fires: an artificial intelligence approach. [Article]. Fire Ecology, 11(1), 106-118. doi: 10.4996/fireecology.1101106

Castelli, M., De Felice, M., Manzoni, L., & Vanneschi, L. (2015)

Electricity Demand Modelling with Genetic Programming. In F. Pereira, P. Machado, E. Costa & A. Cardoso (Eds.), Progress in Artificial Intelligence (Vol. 9273, pp. 213-225). Berlin: Springer-Verlag Berlin.

Castelli, M., Vanneschi, L., Silva, S., & Ruberto, S. (2015)

How to Exploit Alignment in the Error Space: Two Different GP Models Genetic Programming Theory and Practice XII (pp. 133-148). Heidelberg: Springer.

Machado, P., Heywood, M. I., McDermott, J., Castelli, M., García-Sánchez, P., Burelli, P., ... Sim, K. (Eds.). (2015)

Genetic programming: 18th  European conference, EuroGP 2015 Copenhagen, Denmark, april 8–10, 2015 proceedings. (Lecture Notes in Computer Science). Springer Verlag.

Castelli, M., Trujillo, L., Vanneschi, L., Silva, S., Z-Flores, E., & Legrand, P. (2015)

Geometric Semantic Genetic Programming with Local Search. Paper presented at the Gecco'15: Proceedings of the 2015 Genetic and Evolutionary Computation Conference.

Castelli, M. V., L.; Silva, S.; Agapitos, A.; O'Neill, M. (2014)

Semantic Search-Based Genetic Programming and the Effect of Intron Deletion. [Article]. IEEE Transactions on Cybernetics, 44(1), 103-113. doi: 10.1109/tsmcc.2013.2247754

Castelli, M., & Vanneschi, L. (2014)

Genetic algorithm with variable neighborhood search for the optimal allocation of goods in shop shelves. Operations Research Letters, 42(5), 355-360. doi: 10.1016/j.orl.2014.06.002

Castelli, M., Silva, S., Manzoni, L., & Vanneschi, L. (2014)

Geometric Selective Harmony Search. Information Sciences, 279, 468-482. doi: 10.1016/j.ins.2014.04.001

Castelli, M., Vanneschi, L., & Silva, S. (2014)

Prediction of the Unified Parkinson's Disease Rating Scale assessment using a genetic programming system with geometric semantic genetic operators. Expert Systems with Applications, 41(10), 4608-4616. doi: 10.1016/j.eswa.2014.01.018

Castelli, M., Vanneschi, L., & Silva, S. (2014)

Semantic Search Based Genetic Programming and the Effect of Introns Deletion (vol 44, pg 103, 2014). [Correction]. Ieee Transactions on Cybernetics, 44(4), 565-565. doi: 10.1109/tcyb.2014.2303551

Castelli, M., Vanneschi, L., Silva, S., Agapitos, A., & O'Neill, M. (2014)

Semantic Search-Based Genetic Programming and the Effect of Intron Deletion. Ieee Transactions on Cybernetics, 44(1), 103-113. doi: 10.1109/tsmcc.2013.2247754

Vanneschi, L., Castelli, M., & Silva, S. (2014)

A survey of semantic methods in genetic programming. Genetic Programming and Evolvable Machines, 15(2), 195-214. doi: 10.1007/s10710-013-9210-0

Giacobini, M., Provero, P., Vanneschi, L., & Mauri, G. (2014)

Towards the Use of Genetic Programming for the Prediction of Survival in Cancer. In S. Cagnoni, M. Mirolli & M. Villani (Eds.), Evolution, Complexity and Artificial Life (pp. 177-192): Springer Berlin Heidelberg.

Ingalalli, V., Silva, S., Castelli, M., & Vanneschi, L. (2014)

A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems. In M. Nicolau, K. Krawiec, M. Heywood, M. Castelli, P. García-Sánchez, J. Merelo, V. Rivas Santos & K. Sim (Eds.), Genetic Programming (Vol. 8599, pp. 48-60): Springer Berlin Heidelberg.

Ruberto, S., Vanneschi, L., Castelli, M., & Silva, S. (2014)

ESAGP – A Semantic GP Framework Based on Alignment in the Error Space. In M. Nicolau, K. Krawiec, M. Heywood, M. Castelli, P. García-Sánchez, J. Merelo, V. Rivas Santos & K. Sim (Eds.), Genetic Programming (Vol. 8599, pp. 150-161): Springer Berlin Heidelberg.

Castelli, M., & Vanneschi, L. (2014)

A hybrid harmony search algorithm with variable neighbourhood search for the bin-packing problem. Paper presented at the Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC), 2014, Porto.

Castelli, M., Beretta, S., & Vanneschi, L. (2013)

A hybrid genetic algorithm for the repetition free longest common subsequence problem. Operations Research Letters, 41(6), 644-649. doi: http://dx.doi.org/10.1016/j.orl.2013.09.002.

Castelli, M., Vanneschi, L., & Silva, S. (2013)

Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators. Expert Systems with Applications, 40(17), 6856-6862. doi: http://dx.doi.org/10.1016/j.eswa.2013.06.037.

Manzoni, L., Castelli, M., & Vanneschi, L. (2013)

A new genetic programming framework based on reaction systems. Genetic Programming and Evolvable Machines, 14(4), 457-471. doi: 10.1007/s10710-013-9184-y.

White, D. R., McDermott, J., Castelli, M., Manzoni, L., Goldman, B. W., Kronberger, G., . . . Luke, S. (2013)

Better GP benchmarks: community survey results and proposals. Genetic Programming and Evolvable Machines, 14(1), 3-29. doi: 10.1007/s10710-012-9177-2.

Castelli, M., Castaldi, D., Giordani, I., Silva, S., Vanneschi, L., Archetti, F., & Maccagnola, D. (2013)

An Efficient Implementation of Geometric Semantic Genetic Programming for Anticoagulation Level Prediction in Pharmacogenetics. In L. Correia, L. Reis & J. Cascalho (Eds.), Progress in Artificial Intelligence (Vol. 8154, pp. 78-89): Springer Berlin Heidelberg.

Castelli, M., Silva, S., Vanneschi, L., Cabral, A., Vasconcelos, M., Catarino, L., & Carreiras, J. B. (2013)

Land Cover/Land Use Multiclass Classification Using GP with Geometric Semantic Operators. In A. Esparcia-Alcázar (Ed.), Applications of Evolutionary Computation (Vol. 7835, pp. 334-343): Springer Berlin Heidelberg.

Silva, S., Ingalalli, V., Vinga, S., Carreiras, J. B., Melo, J., Castelli, M., . . . Caldas, J. (2013)

Prediction of Forest Aboveground Biomass: An Exercise on Avoiding Overfitting. In A. Esparcia-Alcázar (Ed.), Applications of Evolutionary Computation (Vol. 7835, pp. 407-417): Springer Berlin Heidelberg.

Vanneschi, L., Castelli, M., Manzoni, L., & Silva, S. (2013)

A New Implementation of Geometric Semantic GP and Its Application to Problems in Pharmacokinetics. In K. Krawiec, A. Moraglio, T. Hu, A. Ş. Etaner-Uyar & B. Hu (Eds.), Genetic Programming (Vol. 7831, pp. 205-216): Springer Berlin Heidelberg.

Castelli, M., Castaldi, D., Vanneschi, L., Giordani, I., Archetti, F., & Maccagnola, D. (2013)

An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics. Paper presented at the Fifteenth annual conference companion on Genetic and evolutionary computation conference companion, Amsterdam, The Netherlands.

Castelli, M., Manzoni, L., & Vanneschi, L. (2012)

Parameter Tuning of Evolutionary Reactions Systems. In T. Soule (Ed.), Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference (pp. 727-734). New York: Assoc Computing Machinery.

Manzoni, L., Castelli, M., & Vanneschi, L. (2012)

Evolutionary Reaction Systems. In M. Giacobini, L. Vanneschi & W. Bush (Eds.), Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (Vol. 7246, pp. 13-25): Springer Berlin Heidelberg.

McDermott, J., Manzoni, L., Jaskowski, W., White, D. R., Castelli, M., Krawiec, K., . . . O'Reilly, U. M. (2012)

Genetic Programming Needs Better Benchmarks. In T. Soule (Ed.), Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference (pp. 791-798). New York: Assoc Computing Machinery.

Castelli, M., Manzoni, L., & Vanneschi, L. (2011)

A method to reuse old populations in genetic algorithms. In S. P. L. Antunes (Ed.), Progress in Artificial Intelligence, 15th  Annual Portuguese Conference on Artificial Intelligence, EPIA 2011 (pp. 138–152). Berlin: Springer.

Castelli, M., Manzoni, L., & Vanneschi, L. (2011)

Multi objective genetic programming for feature construction in classication problems. In e. C. A. Coello et al. (Ed.), Learning and Intelligent OptimizatioN (Vol. 6683/2011, pp. 503-506). Berlin: Springer.

Castelli, M., Manzoni, L., & Vanneschi, L. (2011)

Reinsertion of old genetic material: Second chance GP. In S. P. L. Antunes (Ed.), Progress in Artificial Intelligence, 15th  Annual Portuguese Conference on Artificial Intelligence, EPIA, 2011. Berlin: Springer.

Castelli, M., Manzoni, L., Silva, S., & Vanneschi, L. (2011)

A Quantitative Study of Learning and Generalization in Genetic Programming. In S. Silva, J. A. Foster, M. Nicolau, P. Machado & M. Giacobini (Eds.), Genetic Programming (Vol. 6621, pp. 25-36). Berlin: Springer-Verlag Berlin.

Castelli, M., Manzoni, L., & Vanneschi, L. (2011)

The effect of selection from old populations in genetic algorithms. Paper presented at the GECCO ’11 - 13th  annual conference companion on Genetic and evolutionary computation, New York.

Vanneschi, L., Castelli, M., & Manzoni, L. (2011)

The k landscapes: a tunably difficult benchmark for genetic programming. Paper presented at the GECCO ’11 - 13th  annual conference companion on Genetic and evolutionary computation, New York, USA.

Castelli, M., Manzoni, L., Silva, S., & Vanneschi, L. (2010)

A Comparison of the Generalization Ability of Different Genetic Programming Frameworks 2010 Ieee Congress on Evolutionary Computation (pp. 1-8). New York: IEEE.

Vanneschi, L., Castelli, M., Bianco, S., & Schettini, R. (2010)

Genetic Algorithms for Training Data and Polynomial Optimization in Colorimetric Characterization of Scanners. In C. DiChic, C. Cotta, M. Ebner, A. Ekart, A. I. EsparciaAlcazar, C. K. Goh, J. J. Merelo, F. Neri, M. Preuss, J. Togelius & G. N. Yannakakis (Eds.), Applications of Evolutionary Computation, Pt I, Proceedings (Vol. 6024, pp. 282-291). Berlin: Springer-Verlag.

Archetti, F., Giordani, I., Messina, E., Vanneschi, L., & Castelli, M. (2010)

Genetic programming for feature extraction in supervised learning. Paper presented at the EURO XXIV - Proceedings of the 24th  Conference on Operational Research.

Vanneschi, L., Archetti, F., Castelli, M., & Giordani, I. (2009)

Classification of oncologic data with genetic programming. Journal of Artificial Evolution and Applications, 1(6). doi: 10.1155/2009/848532