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Sara Silva
Identificação
Sara Silva, Docente Convidado
Doutora em Engenharia Informática
(Universidade de Coimbra)
Contactos
ssilva@novaims.unl.pt
Biografia

Sara Silva is currently principal investigator in the Faculty of Sciences of the University of Lisbon (FCUL), invited researcher in Centro de Informática e Sistemas da Universidade de Coimbra (CISUC) and invited assistant professor in NOVA Information Management School (NOVA IMS).

Sara Silva obtained a BSc and MSc in Informatics at the University of Lisbon, and a PhD (2008) in Informatics Engineering at the University of Coimbra, Portugal. Her main research interests are bio-inspired machine learning methods for data mining, like neural networks, genetic algorithms, and particularly genetic programming, which she has applied in several interdisciplinary projects ranging from remote sensing and forest science to epidemiology and medical informatics.

Sara Silva has around 50 peer-reviewed scientific publications, roughly divided as 20% ISI journal articles, 10% book chapters and 70% peer-reviewed conference papers. Five of her publications were distinguished with international awards, and other five were distinguished with nominations. 80% of these distinctions happened in the last five years. She is a member of the editorial board of Genetic Programming and Evolvable Machines, guest editor of two journal special issues, and has been chair of several international conferences on evolutionary computation. Recently she has been appointed editor-in-chief of the Genetic and Evolutionary Computation Conference for 2015, the largest world conference on this field. She is the creator and developer of GPLAB - A Genetic Programming Toolbox for MATLAB.

Publicações
Publicação em Periódicos Científicos
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
Trujillo, L., Munoz, L., Galvan-Lopez, E., & Silva, S. (2016). neat Genetic Programming: Controlling bloat naturally. Information Sciences, 333, 21-43. doi: 10.1016/j.ins.2015.11.010
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. 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., 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
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.
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
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
Capítulo de Livro
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.
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.
Goncalves, I., Silva, S., & Fonseca, C. M. (2015). On the Generalization Ability of Geometric Semantic Genetic Programming. In P. Machado, M. I. Heywood, J. McDermott, M. Castelli, P. GarciaSanchez, P. Burelli, S. Risi & K. Sim (Eds.), Genetic Programming (Vol. 9025, pp. 41-52). Berlin: Springer-Verlag Berlin.
Goncalves, I., Silva, S., & Fonseca, C. M. (2015). Semantic Learning Machine: A Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming. In F. Pereira, P. Machado, E. Costa & A. Cardoso (Eds.), Progress in Artificial Intelligence (Vol. 9273, pp. 280-285). Berlin: Springer-Verlag Berlin.
Munoz, L., Silva, S., & Trujillo, L. (2015). M3GP-Multiclass Classification with GP. In P. Machado, M. I. Heywood, J. McDermott, M. Castelli, P. GarciaSanchez, P. Burelli, S. Risi & K. Sim (Eds.), Genetic Programming (Vol. 9025, pp. 78-91). Berlin: Springer-Verlag Berlin.
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., 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., 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.
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.
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.
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.
Publicações em Atas de Conferência Científica
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).
Vanneschi, L., Castelli, M., & Silva, S. (2010, 7-11 July). Measuring bloat, overfitting and functional complexity in genetic programming. Paper presented at the GECCO 2010 - Genetic and Evolutionary Computation Conference, Portland, Oregon.
Silva, S., & Vanneschi, L. (2009, 8-12 July). Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction. Paper presented at the GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, Montreal.

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