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Jaume Bacardit | |||||||||||||||||||||||
Jaume Bacardit
jaume dot bacardit at nottingham dot ac dot uk |
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Academic role | ||||
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My position as Lecturer in Bioinformatics has been jointly appointed between the Schools of Computer Science
and Biosciences of the University of Nottingham. My role is to facilitate and encourage interdisciplinary
research involving both schools.
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Teaching duties | ||||
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My teaching duties for the academic year 2011/2012 are the following:
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Administrative duties | ||||
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Research | ||||
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In a nutshell, my research is mainly focused on Machine Learning and Data Mining, and their application to bioinformatics and, in general, to any class of biological domains. From the Computer Science point of view I am particularly interested in methods for large-scale data mining as well as the interpretability capacity of machine learning methods. My PhD thesis dealt with the Pittburgh model of Learning Classifier Systems (LCS). Specifically, the thesis had the following objectives:
From 2005 to 2007 I worked as RA applying LCSs to protein structure prediction, in a project called
Robust Prediction with Explanatory Power for Protein Structure and Related Prediction Problems Here is the list externally funded projects where I am PI/CI:
Research team and visitors:
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Professional activities | ||||
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I am the workshops co-chair of the
Genetic And Evolutionary Computation Conference - GECCO2011. I was in the organization committee of the International Workshop on Learning Classifier Systems from 2007 to 2010. I was elected to this role by the LCS community. In 2009 I co-organized the Plant Bioinformatics, Systems and Synthetic Biology Summer School that took place in Nottingham from the 27th to the 31st of July, 2009. Reviewer for the following journals (among others)
Member of the program commitee of following conferences (among others)
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Readings | ||||
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Here you can find the BibTeX references I have collected over the years | ||||
Publications | ||||
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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 A Mixed Discrete-Continuous Attribute List Representation for Large Scale Classification Domains Bacardit, J. and Krasnogor, N. In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO2009), pp. 1155-1162, ACM Press, 2009 p1155-bacardit.pdf Automated Alphabet Reduction for Protein Datasets J. Bacardit, M. Stout, J.D. Hirst, A. Valencia, R.E. Smith and N. Krasnogor BMC Bioinformatics 10:6, 2009 Access to the paper Improving the scalability of rule-based evolutionary learning J. Bacardit, E.K. Burke and N. Krasnogor Memetic Computing journal 1(1):55-67, 2009 paperAttListKR.pdf Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems J. Bacardit and N. Krasnogor Evolutionary Computation Journal, 17(3):307-342, 2009 paperMPLCS.pdf Prediction of Topological Contacts in Proteins Using Learning Classifier Systems Stout, M., Bacardit, J., Hirst, J.D., Smith, R.E. and Krasnogor, N. Soft Computing Journal, 13(3):245-258, 2009 Access to the paper KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems 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. Soft Computing Journal, 13(3):307-318, 2009. Access to the paper
Learning Classifier Systems: Looking Back and Glimsing Ahead J. Bacardit, E. Bernaó-Mansilla, and M.V. Butz 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 Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System J. Bacardit and N. Krasnogor 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 Learning classifier systems for optimization problems: A case study on the fractal travelling salesman problem M. Tabacman, N. Krasnogor, J. Bacardit and I. Loiseau Eleventh International Workshop on Learning Classifier Systems, IWLCS2008 iwlcs2008.pdf Fast Rule Representation for Continuous Attributes in Genetics-Based Machine Learning Bacardit, J. and Krasnogor, N. In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO2008), pp. 1421-1422, ACM Press, 2008 gecco2008.pdf Prediction of Recursive Convex Hull Class Assignments for Protein Residues Stout, M., Bacardit, J., Hirst, J.D. and Krasnogor, N. Bioinformatics, 24(7):916-923, 2008 Access to paper Data Mining in Proteomics with Learning Classifier Systems J. Bacardit, M. Stout, J.D. Hirst and N. Krasnogor Bull, L., Bernado Mansilla, E. and Holmes, J. (eds), Learning Classifier Systems in Data Mining, pages 17-46, Springer, 2008 DataMiningLCS.pdf Data Mining in Learning Classifier Systems: Comparing XCS with GAssist Bacardit, J. and Butz, M.V. 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 Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule Bacardit, J., Goldberg D.E. and Butz, M.V. 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 Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach Learning Classifier System Bacardit, J. and Garrell, J.M. 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 Automated Alphabet Reduction Method with Evolutionary Algorithms for Protein Structure Prediction J. Bacardit, M. Stout, J.D. Hirst, K. Sastry, X. Llorà and N. Krasnogor In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO2007), pp. 346-353, ACM Press, 2007 gecco2007-ar.pdf Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System J. Bacardit and N. Krasnogor Ninth International Workshop on Learning Classifier Systems, IWLCS2006 iwlcs2006.pdf Smart Crossover operator with multiple parents for a Pittsburgh Learning Classifier System J. Bacardit and N. Krasnogor 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 Coordination number prediction using Learning Classifier Systems: Performance and interpretability J. Bacardit, M. Stout, J.D. Hirst, N. Krasnogor and J. Blazewicz In Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO2006), pp. 247-254, ACM Press, 2006 gecco2006-cn.pdf From HP Lattice Models to Real Proteins: coordination number prediction using Learning Classifier Systems M. Stout, J. Bacardit, J.D. Hirst, N. Krasnogor and J. Blazewicz 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 Prediction of Residue Exposure and Contact Number for Simplified HP Lattice Model Proteins Using Learning Classifier Systems M. Stout, J. Bacardit, J.D. Hirst, J. Blazewicz and N. Krasnogor 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 Analysis of the Initialization Stage of a Pittsburgh Approach Learning Classifier System Jaume Bacardit Genetic and Evolutionary Computation Conference 2005, GECCO'05 gecco2005.pdf Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time Jaume Bacardit Doctoral disertation, Ramon Llull University, Barcelona, Catalonia, Spain thesis.pdf Speeding-up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy Jaume Bacardit, David E. Goldberg, Martin V. Butz, Xavier Llorà and Josep M. Garrell 8th International Conference on Parallel Problem Solving from Nature - PPSN VIII ppsn04.pdf Data Mining in Learning Classifier Systems: Comparing XCS with GAssist Jaume Bacardit and Martin V. Butz Seventh International Workshop on Learning Classifier Systems (IWLCS-2004) iwlcs2004b.ps.gz Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule Jaume Bacardit, David E. Goldberg and Martin V. Butz Seventh International Workshop on Learning Classifier Systems (IWLCS-2004) iwlcs2004.ps.gz Analysis and improvements of the Adaptive Discretization Intervals knowledge representation Jaume Bacardit and Josep M. Garrell Genetic and Evolutionary Computation Conference 2004, GECCO'04 gecco2004.ps.gz Experimental Evaluation of Discretization Schemes for Rule Induction Jesus Aguilar--Ruiz, Jaume Bacardit and Federico Divina Genetic and Evolutionary Computation Conference 2004, GECCO'04 gecco2004b.ps.gz Combinando multiples discretizadores para aprendizaje de reglas evolutivo con enfoque de Pittsburgh Jaume Bacardit, Josep M. Garrell and Pere Miralles Proceedings of the "Tercer Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados", pages 192-202 maeb04.ps.gz Development of a Genetic Algorithm to Design and Identify Peptides that can Cross the Blood-Brain Barrier: Design and validation in silico 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. Journal of QSAR and Combinatorial Science. Vol. 22, No. 7, pp. 745--753, Wiley-VCH. Comparison of training set reduction techniques for Pittsburgh approach Genetic Classifier Systems Jaume Bacardit and Josep M. Garrell X Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA2003) caepia2003.ps.gz Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach Learning Classifier System Jaume Bacardit and Josep M. Garrell Sixth International Workshop on Learning Classifier Systems (IWLCS-2003) Chicago, July 2003 iwlcs03.ps.gz Evolving multiple discretizations with adaptive intervals for a Pittsburgh Rule-Based Learning Classifier System Jaume Bacardit and Josep M. Garrell Genetic and Evolutionary Computation Conference 2003, GECCO'03 Lecture Notes in Computer Science 2724, pages 1818--1831, Springer-Verlag gecco03.ps.gz Incremental Learning for Pittsburgh Approach Classifier Systems Jaume Bacardit and Josep M. Garrell Proceedings of the "Segundo Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados", pages 303-311 maeb03.ps.gz Evolution of Multi-Adaptive Discretization Intervals for a Rule-Based Genetic Learning System Jaume Bacardit and Josep M. Garrell Proceedings of the 7th Iberoamerican Conference on Artificial Intelligence (IBERAMIA2002) LNAI vol. 2527, pages 350-360, Springer iberamia02.ps.gz The role of interval initialization in a GBML system with rule representation and adaptive discrete intervals Jaume Bacardit and Josep M. Garrell Proceedings of the 5th Catalan Conference on Artificial Intelligence (CCIA'2002) LNAI vol. 2504, pages 184-195, Springer ccia2002.ps.gz Evolution of Adaptive Discretization Intervals for a Rule-Based Genetic Learning System Jaume Bacardit and Josep M. Garrell Proceedings of the 4th Genetic and Evolutionary Computation converence (GECCO-2002) page 677 (poster page) gecco2002_abstract_page.ps.gz Métodos de generalización para sistemas clasificadores de Pittsburgh Jaume Bacardit and Josep M. Garrell Proceedings of the "Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados (AEB'02)", pages 486-493 aeb02_bacardit+garrell.ps.gz(spanish) | ||||
Pictures | ||||
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Here you can find pictures of some place I have been over the years |
