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Mauro Castelli
Identificação
Mauro Castelli, Professor Auxiliar Convidado
Doctor in Computer Science
(University of Milano Bicocca - Italy)
Contactos
mcastelli@novaims.unl.pt
Biografia

Mauro Castelli was born in Lecco (Italy) on April 18th, 1984. He earned his Bachelor degree cum laude in Computer Science by the University of Milano Bicocca in 2006 with a thesis in the field of Bioinformatic and his Master degree cum laude in 2008 with a thesis that bring together the field of Evolutionary Computation and the field of oncological research. He completed a PhD degree in Computer Science by the University of Milano Bicocca in 2012 with a thesis that presents outstanding contributions in the field of Evolutionary Computation and results that outperform state of the art methods in this field. In one of his published work he has defined a new Evolutionary Algorithm able to outperform classical Machine Learning techniques. During his PhD, he was able to establish an international network of collaborations with leading academic institutions. In particular he spent a period working at the University college of Dublin in the Natural and Computing Research Application group, where he started an innovative work in the field of Genetic Programming. He also collaborated with INESC-ID in Lisboa in the Knowledge Discovery and Bioinformatics (KDBIO) group, where he is involved in the EnviGP project financed by Fundaçao para a Ciencia e a Tecnologia of Portugal. He has also collaborated and has an open collaboration with the the Computer Science and Artificial Intelligence Laboratory of MIT (Massachusetts Institute of Technology). From the academic year 2006/2007, he has also covered the position of assistant lecturer for more than 10 graduate and undergraduate courses at the University of Milano Bicocca and at the University of Bergamo. His current area of scientific activity is in the field of computer science. In particular, he is working in the following areas: Evolutionary Computation, Genetic Programming, Genetic Algorithms, Swarm Intelligence, Artificial Intelligence, Machine Learning, Soft Computing, Heuristics for Combinatorial Optimization.

Unidades Curriculares que leciona na NOVA IMS
- Aprendizagem Profunda
- Big Data in Cloud Platforms
- Computação II
- Computação III
- Deep Learning Methods in Finance
- Descriptive Data Mining
- Machine Learning in Finance
- Metodologias de Investigação
- Métodos Descritivos de Data Mining
Publicações
Publicação em Periódicos Científicos
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
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.; Muñoz, L.; Trujillo, L.; Martínez, Y.; Popovič, 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).
Castelli, M., Cattaneo, G., Manzoni, L., & Vanneschi, L. (2018). A distance between populations for n-points crossover in genetic algorithms. Swarm and Evolutionary Computation. [Advance online publication on 21 august 2018]. DOI: 10.1016/j.swevo.2018.08.007
Hajeka, P., Henriques, R., Castelli, M., Vanneschi, L. (2018). Forecasting Performance of Regional Innovation Systems using Semantic-Based Genetic Programming with Local Search Optimizer. Computers and Operations Research (advanced online on 7 February 2018). Doi: https://doi.org/10.1016/j.cor.2018.02.001
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
Ruberto, S.; Vanneschi, L. & Castelli, M. (2018). Genetic Programming with Semantic Equivalence Classes. Swarm and Evolutionary Computation. [Advanced online publication at 15 June 2018]. Doi: https://doi.org/10.1016/j.swevo.2018.06.001
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, A., Vanneschi, L., Castelli, M., & Vega-Rodriguez, M. A. (2018). Multiobjective Metaheuristic to Design RNA Sequences. IEEE Transactions on Evolutionary Computation. DOI: 10.1109/TEVC.2018.2844116
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., & Trujillo, L. (2018). Alignment-based genetic programming for real life applications. Swarm and Evolutionary Computation. [Advanced online publication on 29 september 2018]. DOI: 10.1016/j.swevo.2018.09.006
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
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., & Popovič, 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., & Popovič, 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 Popovič (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 Aleš Popovič (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
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) 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., & Popovič, 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., & 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. 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
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.
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
Capítulo de Livro
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
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.
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.
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., 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., & 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., 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.
Edição de Livros
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
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
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
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.
Publicações em Atas de Conferência Científica
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., 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
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).
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.
Vanneschi, L., Castelli, M., Costa, E., Re, A., Vaz, H., Lobo, V., & Urbano, P. (2015, April 8-10). Improving Maritime Awareness with Semantic Genetic Programming and Linear Scaling: Prediction of Vessels Position Based on AIS Data. Paper presented at the 18th European Conference on EvoApplications Copenhagen, Denmark.
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., 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. (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., 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., 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.

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