Publicações
Publicação em Periódicos Científicos
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
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
La Cava, W., Silva, S., Danai, K., Spector, L., Vanneschi, L., & Moore, J. H. (2018). Multidimensional genetic programming for multiclass classification. Swarm and Evolutionary Computation. [advanced online publication on 12 april 2018]. DOI: 10.1016/j.swevo.2018.03.015
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
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
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., & 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
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., & 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, 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
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
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
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
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
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
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
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.
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
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.
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.
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., 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.
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.
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
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
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
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., 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., 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.
Farinaccio, A., Vanneschi, L., Giacobini, M., Mauri, G., & Provero, P. (2010, 7-11 July). On the use of genetic programming for the prediction of survival in cancer. Paper presented at the GECCO 2010 - Genetic and Evolutionary Computation Conference, Portland, Oregon.
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"
Manzoni, L., Vanneschi, L., & Mauri, G. (2010, 7-11 July). Definition of a crossover based distance for genetic algorithms. Paper presented at the GECCO 2010 - Genetic and Evolutionary Computation Conference, Portland, Oregon.
Valsecchi, A., Vanneschi, L., & Mauri, G. (2010, 7-11 July). Otimization speed and fair sets of functions. Paper presented at the GECCO 2010 - Genetic and Evolutionary Computation Conference, Portland, Oregon.
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.
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.
Vanneschi, L., Codecasa, D., & Mauri, G. (2010, 7-11 July ). An empirical comparison of parallel and distributed particle swarm optimization methods. Paper presented at the GECCO 2010 - Genetic and Evolutionary Computation Conference, Portland, Oregon.
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.
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.
Vanneschi, L., & Cuccu, G. (2009, 8-12 July ). Variable size population for dynamic optimization with genetic programming. Paper presented at the GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, Montreal.
Vanneschi, L., & Gustafson, S. (2009, 8-12 July). Using crossover based similarity measure to improve genetic programming generalization ability. Paper presented at the GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, Montreal.
Vanneschi, L., Valsecchi, A., & Poli, R. (2009, 8-12 July). Limitations of the fitness-proportional negative slope coefficient as a difficulty measure. Paper presented at the GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, Montreal.