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Dr. Jaume Bacardit



2013

A.L. Swan, K.L. Hillier, J.R. Smith, D. Allaway, S. Liddell, J. Bacardit and A. Mobasheri
Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning
BMC Musculoskeletal Disorders, 14:349, 2013

A.L. Swan, A. Mobasheri,D. Allaway, S. Liddell and J. Bacardit
Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology
OMICS: A Journal of Integrative Biology, 17(12): 595-610, 2013 (Please note, this journal has nothing to do with the Omics Publishing Group)
access to the paper

A. Garcia-Piquer, A. Fornells, J. Bacardit, A. Orriols-Puig and E. Golobardes
Large-Scale Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering
IEEE Transactions on Evolutionary Computation, in press, 2013
access to the paper

M. Franco, N. Krasnogor and J. Bacardit
GAssist vs. BioHEL: Critical Assessment of Two Paradigms of Genetics-based Machine Learning
Soft Computing, 17(6):953-981, June 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, 5(2):95-130, June 2013
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



2012

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
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Analysing BioHEL using challenging boolean functions
M. Franco, N. Krasnogor and J. Bacardit
Evolutionary Intelligence, 5(2):87-102, June 2012
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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



2011

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

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.
Bacardit, J.; Browne, W.; Drugowitsch, J.; Bernad├│-Mansilla, E.; Butz, M.V. (Eds.)
Lecture Notes in Artificial Intelligence 6471, Springer, 2011
Link to the book

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

2010

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


2009


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
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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


2008
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
Bacardit, J.; Bernadˇ-Mansilla, E.; Butz, M.V.; Kovacs, T.; LlorÓ, X.; Takadama, K., Editors
Lecture Notes in Artificial Intelligence 4998, Springer, 2008
Link to the book

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
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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


2007

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. LlorÓ 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

2006

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


2005

Jaume Bacardit
Analysis of the Initialization Stage of a Pittsburgh Approach Learning Classifier System
Genetic and Evolutionary Computation Conference 2005, GECCO'05
gecco2005.pdf


2004

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 LlorÓ 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 Espa˝ol de MetaheurÝsticas, Algoritmos Evolutivos y Bioinspirados", pages 192-202
maeb04.ps.gz


2003

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 Asociaciˇn Espa˝ola 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 Espa˝ol de MetaheurÝsticas, Algoritmos Evolutivos y Bioinspirados", pages 303-311
maeb03.ps.gz



2002

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)



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