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School of Biosciences, Division of
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Jaume Bacardit

Lecturer in Bioinformatics, Faculty of Science

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Research Summary

My research is mainly focused on the development of data mining methods for large-scale problems and their application to challenging real-world problems, particularly (but not restricted to) in… read more

Selected Publications

Current Research

My research is mainly focused on the development of data mining methods for large-scale problems and their application to challenging real-world problems, particularly (but not restricted to) in bioinformatics and biological data mining.

Past Research

My PhD thesis dealt with the Pittburgh model of Learning Classifier Systems (LCS). Specifically, the thesis had the following objectives: * Improving the generalization capacity of the model * Reducing the run-time of the system * Proposing rule-based representations for real-valued attributes

From 2005 to 2007 I worked as a postdoc applying LCSs to Bioinformatics, specifically to Protein Structure Prediction, in an EPSRC-funded project called "Robust Prediction with Explanatory Power for Protein Structure and Related Prediction Problems" under the supervision of Dr. Natalio Krasnogor of School of Computer Science, here in Nottingham

  • BASSEL, G.W, GLAAB, E, MARQUEZ, J, HOLDSWORTH, M.J and BACARDIT, J., 2011. Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets. The Plant Cell. 23, 3101-3116
  • FRANCO, MARIA A., KRASNOGOR, NATALIO and BACARDIT, JAUME, 2011. Modelling the initialisation stage of the ALKR representation for discrete domains and GABIL encoding In: Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO `11. 1291-1298
  • FRANCO, MAR�A A., KRASNOGOR, NATALIO and BACARDIT, JAUME, 2010. Speeding up the evaluation of evolutionary learning systems using GPGPUs In: Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO `10. 1039-1046
  • FRANCO, MARIA, KRASNOGOR, NATALIO and BACARDIT, JAUME, 2010. Analysing bioHEL using challenging boolean functions In: Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO `10. 1855-1862
  • BACARDIT, J., STOUT, M., HIRST, J. D., VALENCIA, A., SMITH, R. E. and KRASNOGOR, N., 2009. Automated alphabet reduction for protein datasets. BMC Bioinformatics. 10(1), 6
  • STOUT, M., BACARDIT, J., HIRST, J.D., SMITH, R.E. and KRASNOGOR, N., 2009. Prediction of Topological Contacts in Proteins Using Learning Classifier Systems Soft Computing. 13(3), 245-258
  • 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. and FERNÁNDEZ, J.C., 2009. KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems Soft Computing. 13(3), 307-318
  • BACARDIT J and KRASNOGOR N, 2009. Performance And Efficiency Of Memetic Pittsburgh Learning Classifier Systems. Evolutionary Computation. 17(3), 307-42
  • JAUME BACARDIT, EDMUND K. BURKE and NATALIO KRASNOGOR, 2009. Improving the scalability of rule-based evolutionary learning Memetic Computing. 1(1), 55-67
  • BACARDIT, JAUME and KRASNOGOR, NATALIO, 2009. A mixed discrete-continuous attribute list representation for large scale classification domains In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO `09. 1155
  • BACARDIT, J, STOUT, M, HIRST, JD, VALENCIA, A, SMITH, RE and KRASNOGOR, N, 2009. Automated Alphabet Reduction For Protein Datasets Bmc Bioinformatics. 10, -
  • STOUT, MICHAEL, BACARDIT, JAUME, HIRST, JONATHAN D and KRASNOGOR, NATALIO, 2008. Prediction of recursive convex hull class assignments for protein residues. Bioinformatics (Oxford, England). 24(7), 916-23
  • J. BACARDIT, M. STOUT, J.D. HIRST and N. KRASNOGOR, 2008. Data Mining in Proteomics with Learning Classifier Systems. In: BULL, L., BERNADO MANSILLA, E. and HOLMES, J, eds., Learning Classifier Systems in Data Mining 125. Springer. 17-46
  • M. TABACMAN, N. KRASNOGOR, J. BACARDIT and I. LOISEAU, 2008. Learning classifier systems for optimization problems: A case study on the fractal travelling salesman problem In: Eleventh International Workshop on Learning Classifier Systems.
  • J. BACARDIT and N. KRASNOGOR, 2008. Fast rule representation for continuous attributes in genetics-based machine learning. In: GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation ACM. 1421-1422
  • J. BACARDIT and N. KRASNOGOR, 2008. Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System. In: J. BACARDIT, E. BERNADO-MANSILLA, M.V. BUTZ, T. KOVACS, X. LLORA and K. TAKADAMA, eds., Present and Future of Learning Classifier Systems: Revised Selected Papers of the 9th and 10th editions of the International Workshop on Learning Classifier Systems 4998. Springer. (In Press.)
  • STOUT, M, BACARDIT, J, HIRST, JD and KRASNOGOR, N, 2008. Prediction Of Recursive Convex Hull Class Assignments For Protein Residues Bioinformatics. 24(7), 916-923
  • BACARDIT, J. and GARRELL, J. M., 2007. Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System LECTURE NOTES IN COMPUTER SCIENCE. NUMB 4399, 59-79
  • BACARDIT, J. and BUTZ, M. V., 2007. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist LECTURE NOTES IN COMPUTER SCIENCE. NUMB 4399, 282-290
  • BACARDIT, J., GOLDBERG, D. E. and BUTZ, M. V., 2007. Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule LECTURE NOTES IN COMPUTER SCIENCE. NUMB 4399, 291-307
  • BACARDIT, J., STOUT, M., HIRST, J. D., SASTRY, K., LLOR, X. and KRASNOGOR, N., 2007. Automated Alphabet Reduction Method with Evolutionary Algorithms for Protein Structure Prediction GECCO -CONFERENCE. CONF 9(VOL 1), 346-353
  • STOUT,M., BACARDIT,J., HIRST,J.D., KRASNOGOR,N. and BLAZEWICZ,J., 2006. From HP Lattice Models to Real Proteins: Coordination Number Prediction Using Learning Classifier Systems. In: Applications of Evolutionary Computing.EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC Budapest, Hungary, April 10-12, 2006 Proceedings Springer; Berlin. 208-220
  • BACARDIT, J., STOUT, M., KRASNOGOR, N., HIRST, J. D. and BLAZEWICZ, J., 2006. Coordination Number Prediction Using Learning Classifier Systems: Performance and interpretability GECCO -CONFERENCE-. CONF 8(VOL 1), 247-254
  • STOUT, M., BACARDIT, J., BLAZEWICZ, J., HIRST, J.D. and KRASNOGOR, N., 2006. Prediction of Residue Exposure and Contact Number for Simplified HP Lattice Model Proteins using Learning Classifier Systems In: 7th International FLINS Conference. 601-608
  • BACARDIT, J. and KRASNOGOR, N., 2006. Smart Crossover Operator with Multiple Parents for a Pittsburgh Learning Classifier System GECCO -CONFERENCE-. CONF 8(VOL 2), 1441-1448
  • BACARDIT, J., 2005. Analysis of the Initialization Stage of a Pittsburgh Approach Learning Classifier System GECCO -CONFERENCE-. CONF 7(VOL 2), 1843-1850
  • BACARDIT, J., GOLDBERG, D. E., BUTZ, M. V., LLORA, X. and GARRELL, J. M., 2004. Speeding-Up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy LECTURE NOTES IN COMPUTER SCIENCE. ISSU 3242, 1021-1031
  • BACARDIT, J. and GARRELL, J. M., 2004. Analysis and Improvements of the Adaptive Discretization Intervals Knowledge Representation LECTURE NOTES IN COMPUTER SCIENCE. ISSU 3103, 726-738
  • AGUILAR-RUIZ, J., BACARDIT, J. and DIVINA, F., 2004. Experimental Evaluation of Discretization Schemes for Rule Induction LECTURE NOTES IN COMPUTER SCIENCE. ISSU 3102, 828-839
  • BACARDIT, J. and GARRELL, J. M., 2003. Evolving Multiple Discretizations with Adaptive Intervals for a Pittsburgh Rule-Based Learning Classifier System LECTURE NOTES IN COMPUTER SCIENCE. ISSU 2724, 1818-1831
  • TEIXIDO, M., BELDA, I., ROSELLO, X., GONZALEZ, S., FABRE, M., LLORA, X., BACARDIT, J., GARRELL, J. M., VILARO, S. and ALBERICIO, F., 2003. Full Paper: Development of a Genetic Algorithm to Design and Identify Peptides that can Cross the Blood-Brain Barrier 1. Design and validation in silico QSAR AND COMBINATORIAL SCIENCE. VOL 22(PART 7), 745-753
  • BACARDIT, J. and GARRELL, J. M., 2002. Evolution of adaptive discretization intervals for rule-based genetic learning system GECCO -CONFERENCE-. 4TH, 677
  • BACARDIT, J. and GARRELL, J. M., 2002. The Role of Interval Initialization in a GBML System with Rule Representation and Adaptive Discrete Intervals LECTURE NOTES IN COMPUTER SCIENCE. ISSU 2504, 184-195
  • BACARDIT, J. and GARRELL, J. M., 2002. Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System LECTURE NOTES IN COMPUTER SCIENCE. ISSU 2527, 350-360

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