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Teaching Staff

Biography

Leonardo Vanneschi is a Full Professor at NOVA IMS. His main research interests involve Machine Learning, Data Science, Complex Systems, and in particular Evolutionary Computation. His work can be broadly partitioned into theoretical studies on the foundations of Evolutionary Computation, and applicative work. The former covers the study of the principles of functioning of Evolutionary Algorithms, with the final objective of developing strategies to outperform the traditional techniques. The latter covers several different fields among which computational biology, image processing, personalized medicine, engineering, logistics, economics and marketing. His work has been consistently recognized and appreciated by the international community from 2000 to nowadays. In 2015, he was honoured with the Award for Outstanding Contributions to Evolutionary Computation in Europe, in the context of EvoStar, the leading European Event on Bio-Inspired Computation. In 2020, he was included in the list of the 2% best researchers in the world, both for the year 2019 and for the entire carreer, according to a study conducted by the Stanford University.

Scientific Publications

Abbona, F., Vanneschi, L., & Giacobini, M. (2022)

Towards a Vectorial Approach to Predict Beef Farm Performance. Applied Sciences, 12(3), 1-16. [1137]. https://doi.org/10.3390/app12031137

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

Cruz, P., Vanneschi, L., Painho, M., & Rita, P. (2022)

Automatic Identification of Addresses: A Systematic Literature Review. ISPRS International Journal of Geo-Information, 11(1), 1-27. https://doi.org/10.3390/ijgi11010011

Koukouraki, E., Vanneschi, L., & Painho, M. (2022)

Few-Shot Learning for Post-Earthquake Urban Damage Detection. Remote Sensing, 14(1), 1-20. [40]. https://doi.org/10.3390/rs14010040

Rebuli, K. B., & Vanneschi, L. (2022)

An Empirical Study of Progressive Insular Cooperative GP. SN Computer Science, 3(2), 1-16. [119]. https://doi.org/10.1007/s42979-021-00998-7

Rodrigues, N. M., Malan, K. M., Ochoa, G., Vanneschi, L., & Silva, S. (2022)

Fitness landscape analysis of convolutional neural network architectures for image classification. Information Sciences, 609(September), 711-726. https://doi.org/10.1016/j.ins.2022.07.040

Azzali, I., Cilia, N. D., De Stefano, C., Fontanella, F., Giacobini, M., & Vanneschi, L. (2022)

Vectorial GP for Alzheimer’s Disease Prediction Through Handwriting Analysis. In J. L. Jiménez LaredoJ, J. I. Hidalgo, & K. O. Babaagba (Eds.), Applications of Evolutionary Computation: 25th  European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (pp. 517-530). (Lecture Notes in Computer Science; Vol. 13224). Springer. https://doi.org/10.1007/978-3-031-02462-7_33

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

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

Rebuli, K. B., Giacobini, M., Tallone, N., & Vanneschi, L. (2022)

A preliminary study of prediction interval methods with genetic programming. In GECCO’22 Companion: Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion (pp. 530-533) (GECCO 2022 - The Genetic and Evolutionary Computation Conference, July 9-13, Boston, USA).. Association for Computing Machinery (ACM). ISBN: 987-1-4503-9268-6. https://doi.org/10.1145/3520304.3528806

Rodrigues, N. M., Batista, J. E., La Cava, W., Vanneschi, L., & Silva, S. (2022)

SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. In E. Medvet, G. Pappa, & B. Xue (Eds.), Genetic Programming: 25th  European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings (pp. 68-84). (Lecture Notes in Computer Science; Vol. 13223). Springer. https://doi.org/10.1007/978-3-031-02056-8_5

Zoppi, G., Vanneschi, L., & Giacobini, M. (2022)

Reducing the Number of Training Cases in Genetic Programming. In 2022 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). (CEC 2022 - 2022 IEEE Congress on Evolutionary Computation (CEC), part of IEEE World Congress on Computational Intelligence (IEEE WCCI 2022), 18/07/22 - 23/07/22, Padua, Italy). IEEE. https://doi.org/10.1109/CEC55065.2022.9870327

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.

Batista, J. E., Cabral, A. I. R., Vasconcelos, M. J. P., Vanneschi, L., & Silva, S. (2021)

Improving Land Cover Classification Using Genetic Programming for Feature Construction. Remote Sensing, 13(9), [1623]. https://doi.org/10.3390/rs13091623

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

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

Vanneschi, L. & Pinto, D. C. (2021)

Understanding over-indebtedness in Portugal: descriptive and predictive models. Lisboa: Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa. NOVA Information Management School (NOVA IMS). ISBN: 978-972-8093-20-4. Link: http://hdl.handle.net/10362/114093

Brotto Rebuli, K., & Vanneschi, L. (2021)

Progressive Insular Cooperative GP. In T. Hu, N. Lourenço, & E. Medvet (Eds.), Genetic Programming: 24th  European Conference, EuroGP 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings (pp. 19-35). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12691 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72812-0_2

Abbona, F., Vanneschi, L., Bona, M., & Giacobini, M. (2020)

Towards modelling beef cattle management with Genetic Programming. Livestock Science, 241, 1-12. [104205]. https://doi.org/10.1016/j.livsci.2020.104205

Azzali, I., Vanneschi, L., Bakurov, I., Silva, S., Ivaldi, M., & Giacobini, M. (2020)

Towards the use of vector based GP to predict physiological time series. Applied Soft Computing Journal, 89(April), [106097]. https://doi.org/10.1016/j.asoc.2020.106097

Azzali, I., Vanneschi, L., Mosca, A., Bertolotti, L., & Giacobini, M. (2020)

Towards the use of genetic programming in the ecological modelling of mosquito population dynamics. Genetic Programming And Evolvable Machines. [Advanced online publication on 3 january 2020]. doi: https://doi.org/10.1007/s10710-019-09374-0

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

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

Rodrigues, N. M., Silva, S., & Vanneschi, L. (2020)

A Study of Generalization and Fitness Landscapes for Neuroevolution. IEEE Access, 8, 108216-108234. [9113453]. https://doi.org/10.1109/ACCESS.2020.3001505

Silva, J. M. da, Figueiredo, A., Cunha, J., Eiras-Dias, J. E., Silva, S., Vanneschi, L., & Mariano, P. (2020)

Using rapid chlorophyll fluorescence transients to classify vitis genotypes. Plants, 9(2), 1-19. [174]. https://doi.org/10.3390/plants9020174

Abbona, F., Vanneschi, L., Bona, M., & Giacobini, M. (2020)

A GP approach for precision farming. In 2020 IEEE Congress on Evolutionary Computation, CEC : 2020 Conference Proceedings (pp. 1-8). [9185637] (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185637

Azzali, I., Vanneschi, L., & Giacobini, M. (2020)

Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming. 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. 52-67). (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_4

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

Carrasquinha, E., Santinha, J., Mongolin, A., Lisitskiya, M., Ribeiro, J., Cardoso, F., Matos, C., Vanneschi, L., & Papanikolaou, N. (2020)

Regularization techniques in radiomics: A case study on the prediction of pCR in breast tumours and the axilla. In P. Cazzaniga, D. Besozzi, I. Merelli, & L. Manzoni (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics: 16th  International Meeting, CIBB 2019, Revised Selected Papers (pp. 271-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12313 LNBI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63061-4_24

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

Lopez, U., Trujillo, L., Silva, S., Vanneschi, L., & Legrand, P. (2020)

Unlabeled multi-target regression with genetic programming. In GECCO 2020: Proceedings of the 2020 Genetic and Evolutionary Computation Conference (pp. 976-984). (GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference). Association for Computing Machinery. https://doi.org/10.1145/3377930.3389846

Rodrigues, N. M., Silva, S., & Vanneschi, L. (2020)

A Study of Fitness Landscapes for Neuroevolution. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020: Conference Proceedings [9185783] (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC48606.2020.9185783

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., 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

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

La Cava, W., Silva, S., Danai, K., Spector, L., Vanneschi, L., & Moore, J. H. (2019)

Multidimensional genetic programming for multiclass classification. Swarm and Evolutionary Computation, 44(February), 260-272. [advanced online publication on 12 april 2018]. DOI: 10.1016/j.swevo.2018.03.015

Moreira, J. M., Santiago, I., Santinha, J., Figueiredo, N., Marias, K., Figueiredo, M., ... Papanikolaou, N. (2019)

Challenges and Promises of Radiomics for Rectal Cancer. Current Colorectal Cancer Reports, 15(6), 175-180. https://doi.org/10.1007/s11888-019-00446-y

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

Azzali, I., Vanneschi, L., Silva, S., Bakurov, I., & Giacobini, M. (2019)

A Vectorial Approach to Genetic Programming. In N. Lourenço, T. Hu, H. Richter, L. Sekanina, & P. García-Sánchez (Eds.), Genetic Programming: 22nd  European Conference, EuroGP 2019, Held as Part of EvoStar 2019, Proceedings (pp. 213-227). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11451 LNCS). Switzerland: Springer Verlag. https://doi.org/10.1007/978-3-030-16670-0_14

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

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.

Cabral, A. I. R., Silva, S., Silva, P. C., Vanneschi, L., & Vasconcelos, M. J. (2018)

Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees. ISPRS Journal of Photogrammetry and Remote Sensing, 142, 94-105. DOI: 10.1016/j.isprsjprs.2018.05.007

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

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

Shrestha, S. & Vanneschi, L. (2018)

Improved Fully Convolutional Networks with 2 Conditional Random Fields for Building Extraction. Remote sensing, 10(17), 1135. doi: https://doi.org/10.3390/rs10071135

Silva, S., Vanneschi, L., Cabral, A. I. R., & Vasconcelos, M. J. (2018)

A semi-supervised Genetic Programming method for dealing with noisy labels and hidden overfitting. Swarm and Evolutionary Computation, 39(April), 323-338. DOI: 10.1016/j.swevo.2017.11.003

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

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

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

Bartashevich, P., Bakurov, I., Mostaghim, S., & Vanneschi, L. (2018)

PSO-based search rules for aerial swarms against unexplored vector fields via genetic programming. In Parallel Problem Solving from Nature – PPSN XV: 15th  International Conference, 2018, Proceedings (pp. 41-53). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11101 LNCS). [15th  International Conference on Parallel Problem Solving from Nature, PPSN 2018, 8 to 12 september 2018, Coimbra, Portugal] Springer Verlag. DOI: 10.1007/978-3-319-99253-2_4

Bartashevich, P., Mostaghim, S., Bakurov, I., & Vanneschi, L. (2018)

Evolving PSO algorithm design in vector fields using geometric semantic GP. In GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 262-263). New York: Association for Computing Machinery, Inc. DOI: 10.1145/3205651.3205760

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

La Cava, W.; Silva, S.; Danai, K.; Spector, L.; Vanneschi, L. & Moore, J. H. (2018)

A multidimensional genetic programming approach for identifying epsistatic gene interactions. GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 23-24. (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., 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, 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

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

La Cava, W., Vanneschi, L., Spector, L., Moore, J., & Silva, S. G. O. D. (2017)

Genetic programming representations for multi-dimensional feature learning in biomedical classification. In Applications of Evolutionary Computation - 20th  European Conference, EvoApplications 2017, Proceedings (Vol. 10199 LNCS, pp. 158-173). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10199 LNCS). Springer-Verlag. DOI: 10.1007/978-3-319-55849-3_11

Vanneschi, L. (2017)

An introduction to geometric semantic genetic programming. In O. Schütze, L. Trujillo, P. Legrand, & Y. Maldonado (Eds.), NEO 2015 : Results of the Numerical and Evolutionary Optimization Workshop NEO 2015 held at September 23-25 2015 in Tijuana, Mexico (Vol. 663, pp. 3-42). (Studies in Computational Intelligence). DOI: 10.1007/978-3-319-44003-3_1

Vanneschi, L., & Galvao, B. (2017)

A parallel and distributed semantic Genetic Programming system. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 121-128). [7969304] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/CEC.2017.7969304

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

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

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.

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

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.

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

Valsecchi, A., Vanneschi, L., & Mauri, G. (2014)

A study of search algorithms' optimization speed. Journal of Combinatorial Optimization, 27(2), 256-270. doi: 10.1007/s10878-012-9514-7

Vanneschi, L. (2014)

Improving genetic programming for the prediction of pharmacokinetic parameters. Memetic Computing, 6(4), 255-262. doi: 10.1007/s12293-014-0143-9

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.

Vanneschi, L., Mondini, M., Bertoni, M., Ronchi, A., & Stefano, M. (2013)

Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches. Genetic Programming and Evolvable Machines, 14(4), 431-455. doi: 10.1007/s10710-013-9183-z

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.

Manzoni, L., Vanneschi, L., & Mauri, G. (2012)

A distance between populations for one-point crossover in genetic algorithms. Theoretical Computer Science, 429, 213-221. doi: 10.1016/j.tcs.2011.12.041

Silva, S., & Vanneschi, L. (2012)

Bloat free Genetic Programming: application to human oral bioavailability prediction. International Journal of Data Mining and Bioinformatics, 6(6), 585-601. doi: 10.1504/ijdmb.2012.050266

Silva, S., Dignum, S., & Vanneschi, L. (2012)

Operator equalisation for bloat free genetic programming and a survey of bloat control methods. Genetic Programming and Evolvable Machines, 13(2), 197-238. doi: 10.1007/s10710-011-9150-5

Valsecchi, A., Vanneschi, L., & Mauri, G. (2012)

A study of search algorithms’ optimization speed. Journal of Combinatorial Optimization, 1-15. doi: 10.1007/s10878-012-9514-7

Vanneschi, L., & Mauri, G. (2012)

A study on learning robustness using asynchronous 1D cellular automata rules. Natural Computing, 11(2), 289-302. doi: 10.1007/s11047-012-9311-3

Vanneschi, L., Pirola, Y., Mauri, G., Tomassini, M., Collard, P., & Verel, S. (2012)

A study of the neutrality of Boolean function landscapes in genetic programming. Theoretical Computer Science, 425, 34-57. doi: 10.1016/j.tcs.2011.03.011

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.

Vanneschi, L., Codecasa, D., & Mauri, G. (2012)

An Empirical Study of Parallel and Distributed Particle Swarm Optimization. In F. F. deVega, J. I. H. Perez & J. Lanchares (Eds.), Parallel Architectures and Bioinspired Algorithms (Vol. 415, pp. 125-150). Berlin: Springer-Verlag Berlin.

Vanneschi, L., Mondini, M., Bertoni, M., Ronchi, A., & Stefano, M. (2012)

GeNet: A Graph-Based Genetic Programming Framework for the Reverse Engineering of Gene Regulatory Networks. In M. Giacobini, L. Vanneschi & W. Bush (Eds.), Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (Vol. 7246, pp. 97-109): Springer Berlin Heidelberg.

Silva, S., Dignum, S., & Vanneschi, L. (2011)

Operator equalisation for bloat free genetic programming and a taxonomy of bloat control methods. Genetic Programming and Evolvable Machines, 1-42. doi: 10.1007/s10710-011-9150-5

Vanneschi, L., Farinaccio, A., Mauri, G., Antoniotti, M., Provero, P., & Giacobini, M. (2011)

A comparison of machine learning techniques for survival prediction in breast cancer. BioData Mining, 4, 1-12. doi: 10.1186/1756-0381-4-12

Vanneschi, L., Godecasa, D., & Mauri, G. (2011)

A Comparative Study of Four Parallel and Distributed PSO Methods. New Generation Computing, 29(2), 129-161. doi: 10.1007/s00354-010-0102-z

Vanneschi, L., Mussi, L., & Cagnoni, S. (2011)

Hot topics in Evolutionary Computation. Intelligenza Artificiale, 5(1), 5-17. doi: 10.3233/IA-2011-0001

Cagnoni, S., & Vanneschi, L. (2011)

Evolutionary computation: a brief overview. In e. S. L. Smith and S. Cagnoni (Ed.), (pp. 3-15): John Wiley and Sons.

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.

Farinaccio, A., Vanneschi, L., Provero, P., Mauri, G., & Giacobini, M. (2011)

A New Evolutionary Gene Regulatory Network Reverse Engineering Tool. In C. Pizzuti, M. D. Ritchie & M. Giacobini (Eds.), Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (Vol. 6623, pp. 13-24). Berlin: Springer-Verlag Berlin.

McDermott, J., O'Reilly, U. M., Vanneschi, L., & Veeramachaneni, K. (2011)

How Far Is It from Here to There? A Distance That Is Coherent with GP Operators. In S. Silva, J. A. Foster, M. Nicolau, P. Machado & M. Giacobini (Eds.), Genetic Programming (Vol. 6621, pp. 190-202). Berlin: Springer-Verlag Berlin.

Silva, S., & Vanneschi, L. (2011)

The Importance of Being Flat: Studying the Program Length Distributions of Operator Equalisation. In e. a. R. Riolo, editors (Ed.), Genetic Programming Theory and Practice IX (pp. 211-233). Berlin: Springer.

Trujillo, L., Silva, S., Legrand, P., & Vanneschi, L. (2011)

An Empirical Study of Functional Complexity as an Indicator of Overfitting in Genetic Programming. In S. Silva, J. A. Foster, M. Nicolau, P. Machado & M. Giacobini (Eds.), Genetic Programming (Vol. 6621, pp. 262-273). Berlin: Springer-Verlag Berlin.

Vanneschi, L., & Cuccu, G. (2011)

Reconstructing Dynamic Target Functions by Means of Genetic Programming Using Variable Population Size. In K. Madani, A. D. Correia, A. Rosa & J. Filipe (Eds.), Computational Intelligence (Vol. 343, pp. 121-134). Berlin: Springer-Verlag Berlin.

Vanneschi, L., Codecasa, D., & Mauri, G. (2011)

A Study of Parallel and Distributed Particle Swarm Optimization. In e. a. F. Fernandez, editors (Ed.), BADS '10 Proceedings of the 2nd  workshop on Bio-inspired algorithms for distributed systems (pp. 9-16). Berlin-Heidelberg: Springer.

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.

Archetti, F., Giordani, I., & Vanneschi, L. (2010)

Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset. Computers & Operations Research, 37(8), 1395-1405. doi: 10.1016/j.cor.2009.02.015.

Archetti, F., Giordani, I., & Vanneschi, L. (2010)

Genetic programming for QSAR investigation of docking energy. Applied Soft Computing, 10(1), 170-182. doi: 10.1016/j.asoc.2009.06.013.

O’Neill, M., Vanneschi, L., Gustafson, S., & Banzhaf, W. (2010)

Open issues in genetic programming. Genetic Programming and Evolvable Machines, 11(3-4), 339-363.

Poli, R., Vanneschi, L., Langdon, W. B., & McPhee, N. F. (2010)

Theoretical results in genetic programming: the next ten years? [Article]. Genetic Programming and Evolvable Machines, 11(3-4), 285-320. doi: 10.1007/s10710-010-9110-5.

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.

Silva, S., & Vanneschi, L. (2010)

State-of-the-art genetic programming for predicting human oral bioavailability of drugs. In e. M. P. Rocha et al. (Ed.), Advances in Bioinformatics: 4th  International Workshop on Practical Applications of Computational Biology and Bioinformatics 2010 (IWPACBB 2010) (pp. 165–173): Springer.

Valsecchi, A., Vanneschi, L., & Mauri, G. (2010)

A Study on the Automatic Generation of Asynchronous Cellular Automata Rules by Means of Genetic Algorithms. In S. Bandini, S. Manzoni, H. Umeo & G. Vizzari (Eds.), Cellular Automata (Vol. 6350, pp. 429-438). Berlin: Springer-Verlag Berlin.

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.

Vanneschi, L., Farinaccio, A., Giacobini, M., Mauri, G., Antoniotti, M., & Provero, P. (2010)

Identification of Individualized Feature Combinations for Survival Prediction in Breast Cancer: A Comparison of Machine Learning Techniques. In C. Pizzuti, M. D. Ritchie & M. Giacobini (Eds.), Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Proceedings (Vol. 6023, pp. 110-121). 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.

Farinaccio, A., Vanneschi, L., Provero, P., Mauri, G., & Giacobini, M. (2010)

A study on gene regulatory network reconstruction and simulation. Paper presented at the WIRN 2010 - 20th  Italian Workshop on Neural Networks, special session on "The Dynamics of Biological Networks"

Vanneschi, L., Codecasa, D., & Mauri, G. (2010)

A study of parallel and distributed particle swarm optimization methods. Paper presented at the BADS ’10 - 2nd  workshop on Bio-inspired algorithms for distributed systems, New York, USA.

Bandini, S., Vanneschi, L., Wuensche, A., & Shehata, A. B. (2009)

Cellular automata pattern recognition and rule evolution through a neuro-genetic approach. Journal of Cellular Automata, 4(3), 171-181.

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

Bianco, S., Schettini, R., & Vanneschi, L. (2009)

Empirical modelling for colorimetric characterization of digital cameras 2009 16th  IEEE International Conference on Image Processing, Vols 1-6 (pp. 3433-3436). New York: IEEE.

Vanneschi, L., & Cuccu, G. (2009)

A study of genetic programming variable population size for dynamic optimization problems. In A. Dourado, A. Rosa & K. Madani (Eds.), IJCCI 2009: Proceedings of the International Joint Conference on Computational Intelligence (pp. 119-126). Setubal: Insticc-Inst Syst Technologies Information Control & Communication.

Vanneschi, L., & Poli, R. (2009)

Genetic programming: Introduction, applications, theory and open issues. In G. Rozenberg, T. Bäck & J. N. e. s. Kok (Eds.), Handbook on Natural Computing.

Vanneschi, L., & Silva, S. (2009)

Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming. In L. S. Lopes, N. Lau, P. Mariano & L. M. Rocha (Eds.), Progress in Artificial Intelligence, Proceedings (Vol. 5816, pp. 65-76). Berlin: Springer-Verlag Berlin.

Vanneschi, L., Verel, S., Tomassini, M., & Collard, P. (2009)

NK Landscapes Difficulty and Negative Slope Coefficient: How Sampling Influences the Results. In M. Giacobini, A. Brabazon, S. Cagnoni, G. A. DiCaro, A. Ekart, A. I. EsparciaAlcazar, M. Farooq, A. Fink, P. Machado, J. McCormack, M. Oneill, F. Neri, M. Preuss, F. Rothlauf, E. Tarantino & S. Yang (Eds.), Applications of Evolutionary Computing, Proceedings (Vol. 5484, pp. 645-654). Berlin: Springer-Verlag Berlin.

Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D., & Vanneschi, L. (2009)

A Comparison of Genetic Algorithms and Particle Swarm Optimization for Parameter Estimation in Stochastic Biochemical Systems. In C. R. M. D. G. M. Pizzuti (Ed.), Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Proceedings (Vol. 5483, pp. 116-127).

Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D., & Vanneschi, L. (2009)

A study of particle swarm optimization for parameters estimation in biochemical systems. Paper presented at the Proceedings of the SysBioHealth Symposium 2009.

Farinaccio, A., Vanneschi, L., Muppirisetty, S., Giacobini, M., Antoniotti, M., Mauri, G., & Provero, P. (2009)

Genetic programming for survival prediction in breast cancer. Paper presented at the Proceedings of the SysBioHealth Symposium 2009.