Leonardo Vanneschi

  • Friday, 18 July 2014 14:38

leonardo minIdentification

Leonardo Vanneschi, Associate Professor,
Doctor in Computer Science (University of Lausanne - Switzerland)

Contacts

Telephone: 213828610209
Fax: 213828611
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Biography

Leonardo Vanneschi is an Associate Professor with Tenure ("Professor Associado com Agregação") at the NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Portugal. His main research interests involve Machine Learning, Complex Systems, Data Mining, 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 able to outperform the traditional techniques. The latter covers several different fields among which computational biology, image processing, personalized medicine, engineering, economics and maritime safety and security. 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.

Publications

Journal Article

  • 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

Book Section

  • 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.

Conference Proceeding

  • 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
  • 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.
  • arinaccio, 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.
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