Jaume Bacardit

Dan Andrei Calian, Jaume Bacardit: Integrating memetic search into the BioHEL evolutionary learning system for large-scale datasets. Memetic Computing 5(2): 95-130 (2013)

María A. Franco, Natalio Krasnogor, Jaume Bacardit: GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning. Soft Comput. 17(6): 953-981 (2013)

Jaume Bacardit, Xavier Llorà: Large-scale data mining using genetics-based machine learning. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 3(1): 37-61 (2013)

Jaume Bacardit, Pawel Widera, Alfonso E. Márquez Chamorro, Federico Divina, Jesús S. Aguilar-Ruiz, Natalio Krasnogor: Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features. Bioinformatics 28(19): 2441-2448 (2012)

María A. Franco, Natalio Krasnogor, Jaume Bacardit: Analysing BioHEL using challenging boolean functions. Evolutionary Intelligence 5(2): 87-102 (2012)

Alfonso E. Márquez Chamorro, Federico Divina, Jesús S. Aguilar-Ruiz, Jaume Bacardit, Gualberto Asencio Cortés, Cosme Ernesto Santiesteban Toca: A NSGA-II Algorithm for the Residue-Residue Contact Prediction. EvoBIO 2012: 234-244

María A. Franco, Natalio Krasnogor, Jaume Bacardit: Post-processing operators for decision lists. GECCO 2012: 847-854

Jaume Bacardit, Xavier Llorà: Large scale data mining using genetics-based machine learning. GECCO (Companion) 2012: 1171-1196

Jaume Bacardit, Xavier Llorà: Large scale data mining using genetics-based machine learning. GECCO (Companion) 2011: 1285-1310

María A. Franco, Natalio Krasnogor, Jaume Bacardit: Modelling the initialisation stage of the ALKR representation for discrete domains and GABIL encoding. GECCO 2011: 1291-1298

Robert Elliott Smith, Max Kun Jiang, Jaume Bacardit, Michael Stout, Natalio Krasnogor, Jonathan D. Hirst: A learning classifier system with mutual-information-based fitness. Evolutionary Intelligence 3(1): 31-50 (2010)

Jaume Bacardit, Xavier Llorà: Guest Editorial: Thematic Issue on 'Metaheuristics for large scale data mining'. Memetic Computing 2(3): 163-164 (2010)

María A. Franco, Natalio Krasnogor, Jaume Bacardit: Speeding up the evaluation of evolutionary learning systems using GPGPUs. GECCO 2010: 1039-1046

María A. Franco, Natalio Krasnogor, Jaume Bacardit: Analysing bioHEL using challenging boolean functions. GECCO (Companion) 2010: 1855-1862

Pawel Widera, Jaume Bacardit, Natalio Krasnogor, Carlos García-Martínez, Manuel Lozano: Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology. GECCO (Companion) 2010: 1991-1998

Jaume Bacardit, Will N. Browne, Jan Drugowitsch, Ester Bernadó-Mansilla, Martin V. Butz (Eds.): Learning Classifier Systems - 11th International Workshop, IWLCS 2008, Atlanta, GA, USA, July 13, 2008, and 12th International Workshop, IWLCS 2009, Montreal, QC, Canada, July 9, 2009, Revised Selected Papers. Lecture Notes in Computer Science 6471, Springer 2010, isbn 978-3-642-17507-7

Jaume Bacardit, Michael Stout, Jonathan D. Hirst, Alfonso Valencia, Robert Elliott Smith, Natalio Krasnogor: Automated Alphabet Reduction for Protein Datasets. BMC Bioinformatics 10 (2009)

Jaume Bacardit, Natalio Krasnogor: Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems. Evolutionary Computation 17(3): 307-342 (2009)

Jaume Bacardit, Edmund K. Burke, Natalio Krasnogor: Improving the scalability of rule-based evolutionary learning. Memetic Computing 1(1): 55-67 (2009)

Michael Stout, Jaume Bacardit, Jonathan D. Hirst, Robert Elliott Smith, Natalio Krasnogor: Prediction of topological contacts in proteins using learning classifier systems. Soft Comput. 13(3): 245-258 (2009)

Jesús Alcalá-Fdez, Luciano Sánchez, Salvador García, María José del Jesús, Sebastián Ventura, Josep Maria Garrell i Guiu, José Otero, Cristóbal Romero, Jaume Bacardit, Víctor M. Rivas, Juan Carlos Fernández, Francisco Herrera: KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput. 13(3): 307-318 (2009)

Jaume Bacardit, Natalio Krasnogor: A mixed discrete-continuous attribute list representation for large scale classification domains. GECCO 2009: 1155-1162

Jaume Bacardit, Xavier Llorà: Large scale data mining using genetics-based machine learning. GECCO (Companion) 2009: 3381-3412

Michael Stout, Jaume Bacardit, Jonathan D. Hirst, Natalio Krasnogor: Prediction of recursive convex hull class assignments for protein residues. Bioinformatics 24(7): 916-923 (2008)

Jaume Bacardit, Natalio Krasnogor: Fast rule representation for continuous attributes in genetics-based machine learning. GECCO 2008: 1421-1422

Maximiliano Tabacman, Natalio Krasnogor, Jaume Bacardit, Irene Loiseau: Learning classifier systems for optimisation problems: a case study on fractal travelling salesman problem. GECCO (Companion) 2008: 2039-2046

Jaume Bacardit, Michael Stout, Jonathan D. Hirst, Natalio Krasnogor: Data Mining in Proteomics with Learning Classifier Systems. Learning Classifier Systems in Data Mining 2008: 17-46

Jaume Bacardit, Ester Bernadó-Mansilla, Martin V. Butz, Tim Kovacs, Xavier Llorà, Keiki Takadama (Eds.): 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 Papers. Lecture Notes in Computer Science 4998, Springer 2008, isbn 978-3-540-88137-7

Jaume Bacardit, Michael Stout, Jonathan D. Hirst, Kumara Sastry, Xavier Llorà, Natalio Krasnogor: Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. GECCO 2007: 346-353

Jaume Bacardit, Ester Bernadó-Mansilla, Martin V. Butz: Learning Classifier Systems: Looking Back and Glimpsing Ahead. IWLCS 2007: 1-21

Jaume Bacardit, Natalio Krasnogor: Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System. IWLCS 2007: 255-268

Michael Stout, Jaume Bacardit, Jonathan D. Hirst, Natalio Krasnogor, Jacek Blazewicz: From HP Lattice Models to Real Proteins: Coordination Number Prediction Using Learning Classifier Systems. EvoWorkshops 2006: 208-220

Jaume Bacardit, Michael Stout, Natalio Krasnogor, Jonathan D. Hirst, Jacek Blazewicz: Coordination number prediction using learning classifier systems: performance and interpretability. GECCO 2006: 247-254

Jaume Bacardit, Natalio Krasnogor: Smart crossover operator with multiple parents for a Pittsburgh learning classifier system. GECCO 2006: 1441-1448

Jaume Bacardit: Analysis of the initialization stage of a Pittsburgh approach learning classifier system. GECCO 2005: 1843-1850

Jaume Bacardit, Josep Maria Garrell i Guiu: Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System. IWLCS 2005: 59-79

Jaume Bacardit, Martin V. Butz: Data Mining in Learning Classifier Systems: Comparing XCS with GAssist. IWLCS 2005: 282-290

Jaume Bacardit, David E. Goldberg, Martin V. Butz: Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule. IWLCS 2005: 291-307

Jaume Bacardit, Josep Maria Garrell i Guiu: Analysis and Improvements of the Adaptive Discretization Intervals Knowledge Representation. GECCO (2) 2004: 726-738

Jesús S. Aguilar-Ruiz, Jaume Bacardit, Federico Divina: Experimental Evaluation of Discretization Schemes for Rule Induction. GECCO (1) 2004: 828-839

Jaume Bacardit, David E. Goldberg, Martin V. Butz, Xavier Llorà, Josep Maria Garrell i Guiu: Speeding-Up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy. PPSN 2004: 1021-1031

Jaume Bacardit, Josep Maria Garrell i Guiu: Evolving Multiple Discretizations with Adaptive Intervals for a Pittsburgh Rule-Based Learning Classifier System. GECCO 2003: 1818-1831

Jaume Bacardit, Josep Maria Garrell i Guiu: The Role of Interval Initialization in a GBML System with Rule Representation and Adaptive Discrete Intervals. CCIA 2002: 184-195

Jaume Bacardit, Josep Maria Garrell i Guiu: Evolution Of Adaptive Discretization Intervals For A Rule-based Genetic Learning System. GECCO 2002: 677

Jaume Bacardit, Josep Maria Garrell i Guiu: Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System. IBERAMIA 2002: 350-360