Dr. Jaume Bacardit
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| Dr. Jaume Bacardit |
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M. Franco, N. Krasnogor and J. Bacardit GAssist vs. BioHEL: Critical Assessment of Two Paradigms of Genetics-based Machine Learning Soft Computing, in press, 2013 access to the paper D.A. Calian and J. Bacardit Integrating memetic search into the BioHEL evolutionary learning system for large-scale datasets Memetic Computing, in press, 2013, doi: 10.1007/s12293-013-0108-4 access to the paper J. Bacardit and X. Llorà Large-scale data mining using genetics-based machine learning WIREs Data Mining and Knowledge Discovery 2013, 3: 37-61 doi: 10.1002/widm.1078 eprint access to the paper H.P. Fainberg, K. Bodley, J. Bacardit, D. Li, F. Wessely, N.P. Mongan, M.E. Symonds, L. Clarke and A. Mostyn Reduced Neonatal Mortality in Meishan Piglets: A Role for Hepatic Fatty Acids? PLoS ONE 7(11):e49101. doi:10.1371/journal.pone.0049101 access to the paper J. Bacardit, P. Widera, A. Márquez-Chamorro, F. Divina, J.S. Aguilar-Ruiz and Natalio Krasnogor Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features Bioinformatics (2012) 28 (19): 2441-2448. doi:10.1093/bioinformatics/bts472 access to the paper E. Glaab, J. Bacardit, J.M. Garibaldi and N. Krasnogor Using Rule-Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data PLoS ONE 7(7):e39932. 2012. doi:10.1371/journal.pone.0039932 access to the paper Analysing BioHEL using challenging boolean functions M. Franco, N. Krasnogor and J. Bacardit Evolutionary Intelligence, 5(2):87-102, June 2012 access to the paper Post-processing Operators for Decision Lists M. Franco, N. Krasnogor and J. Bacardit In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation - GECCO2012, pages 847-854. ACM, 2012 access to the paper Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets George W. Bassel, Enrico Glaab, Julietta Marquez, Michael J. Holdsworth and Jaume Bacardit The Plant Cell, 23(9):3101-3116, 2011 access to the paper
Modelling the Initialisation Stage of the ALKR Representation for Discrete Domains and GABIL Encoding M. Franco, N. Krasnogor and J. Bacardit In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation - GECCO2011, pages 1291-1298. ACM, 2011 Best Paper Award of the GBML track access to the paper Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology. P. Widera, J. Bacardit, N. Krasnogor, C. Garcia-Martinez, and M. Lozano. In GECCO '10: Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, pages 1991-1998. ACM, 2010 access to the paper Analysing BioHEL Using Challenging Boolean Functions M. Franco, N. Krasnogor and J. Bacardit 13th International Workshop on Learning Classifier Systems - IWLCS 2010 In GECCO '10: Proceedings of the 12th annual conference comp on Genetic and evolutionary computation, pages 1855-1862. ACM, 2010 access to the paper Speeding Up the Evaluation of Evolutionary Learning Systems using GPGPUs M. Franco, N. Krasnogor and J. Bacardit In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO2010), 1039-1046, ACM Press, 2010 Best Paper Award of the GBML track access to the paper A learning classifier system with mutual-information-based fitness R.E. Smith, M.K. Jiang, J. Bacardit, M. Stout, N. Krasnogor and J.D. Hirst Evolutionary Intelligence, 3(1):31-50, 2010 access to the paper Bacardit, J. and Krasnogor, N. A Mixed Discrete-Continuous Attribute List Representation for Large Scale Classification Domains In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO2009), pp. 1155-1162, ACM Press, 2009 p1155-bacardit.pdf J. Bacardit, M. Stout, J.D. Hirst, A. Valencia, R.E. Smith and N. Krasnogor Automated Alphabet Reduction for Protein Datasets BMC Bioinformatics 10:6, 2009 Access to the paper J. Bacardit, E.K. Burke and N. Krasnogor Improving the scalability of rule-based evolutionary learning Memetic Computing journal 1(1):55-67, 2009 paperAttListKR.pdf J. Bacardit and N. Krasnogor Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems Evolutionary Computation Journal, 17(3):307-342, 2009 paperMPLCS.pdf Stout, M., Bacardit, J., Hirst, J.D., Smith, R.E. and Krasnogor, N. Prediction of Topological Contacts in Proteins Using Learning Classifier Systems Soft Computing Journal, 13(3):245-258, 2009 Access to the paper Alcalá-Fdez, J., Sánchez, L., García S., del Jesus, M.J., Ventura, S., Garrell, J.M., Otera, J., Romero, C., Bacardit, J., Rivas, V.M., Fernández, J.C. and Herrera, F. KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems Soft Computing Journal, 13(3):307-318, 2009. Access to the paper
J. Bacardit, E. Bernaó-Mansilla, and M.V. Butz Learning Classifier Systems: Looking Back and Glimsing Ahead Learning Classifier Systems. 10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007, Revised Selected Paper, LNAI 4998, pp. 1-21, 2008, Springer LCS-Looking-Glimsing.pdf J. Bacardit and N. Krasnogor Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System Learning Classifier Systems. 10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007, Revised Selected Paper, LNAI 4998, pp. 255-268, 2008, Springer EnsemblesLCSbook.pdf M. Tabacman, N. Krasnogor, J. Bacardit and I. Loiseau Learning classifier systems for optimization problems: A case study on the fractal travelling salesman problem Eleventh International Workshop on Learning Classifier Systems, IWLCS2008 iwlcs2008.pdf Bacardit, J. and Krasnogor, N. Fast Rule Representation for Continuous Attributes in Genetics-Based Machine Learning In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO2008), pp. 1421-1422, ACM Press, 2008 gecco2008.pdf Stout, M., Bacardit, J., Hirst, J.D. and Krasnogor, N. Prediction of Recursive Convex Hull Class Assignments for Protein Residues Bioinformatics, 24(7):916-923, 2008 Access to paper J. Bacardit, M. Stout, J.D. Hirst and N. Krasnogor Data Mining in Proteomics with Learning Classifier Systems Bull, L., Bernado Mansilla, E. and Holmes, J. (eds), Learning Classifier Systems in Data Mining, pages 17-46, Springer, 2008 DataMiningLCS.pdf Bacardit, J. and Butz, M.V. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist Learning Classifier Systems, Revised Selected Papers of the International Workshop on Learning Classifier Systems 2003-2005, Lecture Notes in Computer Science 4399, pp. 282-290, 2007, Springer bacardit07data.pdf Bacardit, J., Goldberg D.E. and Butz, M.V. Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule Learning Classifier Systems, Revised Selected Papers of the International Workshop on Learning Classifier Systems 2003-2005, Lecture Notes in Computer Science 4399, pp. 291-307, 2007, Springer bacardit07improving.pdf Bacardit, J. and Garrell, J.M. Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach Learning Classifier System Learning Classifier Systems, Revised Selected Papers of the International Workshop on Learning Classifier Systems 2003-2005, Lecture Notes in Computer Science 4399, pp. 59-79, 2007, Springer bacardit07bloat.pdf J. Bacardit, M. Stout, J.D. Hirst, K. Sastry, X. Llorà and N. Krasnogor Automated Alphabet Reduction Method with Evolutionary Algorithms for Protein Structure Prediction In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO2007), pp. 346-353, ACM Press, 2007 gecco2007-ar.pdf J. Bacardit and N. Krasnogor Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System Ninth International Workshop on Learning Classifier Systems, IWLCS2006 iwlcs2006.pdf J. Bacardit and N. Krasnogor Smart Crossover operator with multiple parents for a Pittsburgh Learning Classifier System Genetic and Evolutionary Computation Conference 2006, GECCO'06 In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO2006), pp. 1441 - 1448, ACM Press, 2006 gecco2006-sx.pdf J. Bacardit, M. Stout, J.D. Hirst, N. Krasnogor and J. Blazewicz Coordination number prediction using Learning Classifier Systems: Performance and interpretability In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO2006), pp. 247-254, ACM Press, 2006 gecco2006-cn.pdf M. Stout, J. Bacardit, J.D. Hirst, N. Krasnogor and J. Blazewicz From HP Lattice Models to Real Proteins: coordination number prediction using Learning Classifier Systems 4th European Workshop on Evolutionary Computation and Machine Learning in Bioinformatics 2006 Lecture Notes in Computer Science vol. 3907, pp. 208-220, Springer, 2006 evobio2006.pdf M. Stout, J. Bacardit, J.D. Hirst, J. Blazewicz and N. Krasnogor Prediction of Residue Exposure and Contact Number for Simplified HP Lattice Model Proteins Using Learning Classifier Systems In D. a. Ruan, P. D'hondt, P. F. Fantoni, M. D. Cock, M. Nachtegael and E. E. Kerre, eds., 7th International FLINS Conference on Applied Artificial Intelligence,, World Scientific, 2006, pp. 601-608. CIBB2006.pdf Jaume Bacardit Analysis of the Initialization Stage of a Pittsburgh Approach Learning Classifier System Genetic and Evolutionary Computation Conference 2005, GECCO'05 gecco2005.pdf Jaume Bacardit Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time Doctoral disertation, Ramon Llull University, Barcelona, Catalonia, Spain thesis.pdf Jaume Bacardit, David E. Goldberg, Martin V. Butz, Xavier Llorà and Josep M. Garrell Speeding-up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy 8th International Conference on Parallel Problem Solving from Nature - PPSN VIII ppsn04.pdf Jaume Bacardit and Martin V. Butz Data Mining in Learning Classifier Systems: Comparing XCS with GAssist Seventh International Workshop on Learning Classifier Systems (IWLCS-2004) iwlcs2004b.ps.gz Jaume Bacardit, David E. Goldberg and Martin V. Butz Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule Seventh International Workshop on Learning Classifier Systems (IWLCS-2004) iwlcs2004.ps.gz Jaume Bacardit and Josep M. Garrell Analysis and improvements of the Adaptive Discretization Intervals knowledge representation Genetic and Evolutionary Computation Conference 2004, GECCO'04 gecco2004.ps.gz Jesus Aguilar--Ruiz, Jaume Bacardit and Federico Divina Experimental Evaluation of Discretization Schemes for Rule Induction Genetic and Evolutionary Computation Conference 2004, GECCO'04 gecco2004b.ps.gz Jaume Bacardit, Josep M. Garrell and Pere Miralles Combinando multiples discretizadores para aprendizaje de reglas evolutivo con enfoque de Pittsburgh Proceedings of the "Tercer Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados", pages 192-202 maeb04.ps.gz Teixidó, M., Belda, I., Rosell&ocaute;, X., Gonzàlez, S., Fabre, M., Llorà, X., Bacardit, J., Garrell, J.M., Vilaró, S., Albericio, F., and Giralt, E. Development of a Genetic Algorithm to Design and Identify Peptides that can Cross the Blood-Brain Barrier: Design and validation in silico Journal of QSAR and Combinatorial Science. Vol. 22, No. 7, pp. 745--753, Wiley-VCH. Jaume Bacardit and Josep M. Garrell Comparison of training set reduction techniques for Pittsburgh approach Genetic Classifier Systems X Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA2003) caepia2003.ps.gz Jaume Bacardit and Josep M. Garrell Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach Learning Classifier System Sixth International Workshop on Learning Classifier Systems (IWLCS-2003) Chicago, July 2003 iwlcs03.ps.gz Jaume Bacardit and Josep M. Garrell Evolving multiple discretizations with adaptive intervals for a Pittsburgh Rule-Based Learning Classifier System Genetic and Evolutionary Computation Conference 2003, GECCO'03 Lecture Notes in Computer Science 2724, pages 1818--1831, Springer-Verlag gecco03.ps.gz Jaume Bacardit and Josep M. Garrell Incremental Learning for Pittsburgh Approach Classifier Systems Proceedings of the "Segundo Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados", pages 303-311 maeb03.ps.gz Jaume Bacardit and Josep M. Garrell Evolution of Multi-Adaptive Discretization Intervals for a Rule-Based Genetic Learning System Proceedings of the 7th Iberoamerican Conference on Artificial Intelligence (IBERAMIA2002) LNAI vol. 2527, pages 350-360, Springer iberamia02.ps.gz Jaume Bacardit and Josep M. Garrell The role of interval initialization in a GBML system with rule representation and adaptive discrete intervals Proceedings of the 5th Catalan Conference on Artificial Intelligence (CCIA'2002) LNAI vol. 2504, pages 184-195, Springer ccia2002.ps.gz Jaume Bacardit and Josep M. Garrell Evolution of Adaptive Discretization Intervals for a Rule-Based Genetic Learning System Proceedings of the 4th Genetic and Evolutionary Computation converence (GECCO-2002) page 677 (poster page) gecco2002_abstract_page.ps.gz Jaume Bacardit and Josep M. Garrell Métodos de generalización para sistemas clasificadores de Pittsburgh Proceedings of the "Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados (AEB'02)", pages 486-493 aeb02_bacardit+garrell.ps.gz(spanish) |
