Prof. Jaume Bacardit



2024

Monica T. Hannani, Christian S. Thudium, Alfred C. Gellhornc, Ali Mobasheri, Jaume Bacardit, Anne-Christine Bay-Jensen
Longitudinal stability of molecular endotypes of knee osteoarthritis patients
Osteoarthritis and Cartilage (2024)
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Claire M. Owen, Jaume Bacardit, Maw P. Tan, Nor I. Saedon, Choon-Hian Goh, Julia L. Newton, James Frith.
Artificial intelligence driven clustering of blood pressure profiles reveals frailty in orthostatic hypertension.
Experimental Physilology, 2024.
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Zhou, Ryan, Jaume Bacardit, Alexander Brownlee, Stefano Cagnoni, Martin Fyvie, Giovanni Iacca, John McCall, Niki van Stein, David Walker, and Ting Hu.
Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems
IEEE Transactions on Evolutionary Computation, in press, 2024
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Conor Turner, Luba M. Pardo, David A. Gunn, Ruediger Zillmer, Selma Mekic, Fan Liu, M. Arfan Ikram, Caroline C. W. Klaver, Pauline H. Croll, Andre Goedegebure, Katerina Trajanoska, Fernando Rivadeneira, Maryam Kavousi, Guy G. O. Brusselle, Manfred Kayser, Tamar Nijsten, Jaume Bacardit
Deep learning predicted perceived age is a reliable approach for analysis of facial ageing: A proof of principle study
Journal of the European Academy of Dermatology and Venereology (2024)
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Schmidt, L., Mohamed, S., Meader, N., Bacardit, J., & Craig, D.
Automated data analysis of unstructured grey literature in health research: A mapping review.
Research Synthesis Methods, 15(2), 178-197 (2024).
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Hannani, Monica T., Christian S. Thudium, Morten A. Karsdal, Christoph Ladel, Ali Mobasheri, Melanie Uebelhoer, Jonathan Larkin, Jaume Bacardit, André Struglics, and Anne-Christine Bay-Jensen.
From biochemical markers to molecular endotypes of osteoarthritis: a review on validated biomarkers.
Expert Review of Molecular Diagnostics 24, no. 1-2 (2024): 23-38.
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Yiming Huang, Anil Wipat and Jaume Bacardit
Transcriptional biomarker discovery toward building a load stress reporting system for engineered Escherichia coli strains
Biotechnology and Bioengineering 121.1 (2024): 355-365.
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2023

B. Shirt-Ediss, J. Connolly, J. Elezgaray, E. Torelli, S.A. Navarro, J. Bacardit, N. Krasnogor
Reverse engineering DNA origami nanostructure designs from raw scaffold and staple sequence lists
Computational and Structural Biotechnology Journal, 21(C):3615-3626, 2023
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Schmidt, L., Mohamed, S., Meader, N., Bacardit, J., & Craig, D.
Automated data analysis of unstructured grey literature in health research: A mapping review.
Research Synthesis Methods, in press.
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Pawel Widera, Paco MJ Welsing, Samuel O Danso, Sjaak Peelen, Margreet Kloppenburg, Marieke Loef, Anne C Marijnissen, Eefje M van Helvoort, Francisco J Blanco, Joana Magalhães, Francis Berenbaum, Ida K Haugen, Anne-Christine Bay-Jensen, Ali Mobasheri, Christoph Ladel, John Loughlin, Floris PJG Lafeber, Agnès Lalande, Jonathan Larkin, Harrie Weinans, Jaume Bacardit
Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials
Osteoarthritis and Cartilage Open (2023): 100406.
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Sadaf Iqbal, Jaume Bacardit, Bridget Griffiths, John Allen
Deep learning classification of systemic sclerosis from multi-site photoplethysmography signals
Frontiers in Physiology 14 (2023): 1242807
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Iqbal, Sadaf, Sharad Agarwal, Ian Purcell, Alan Murray, Jaume Bacardit, and John Allen.
Deep learning identification of coronary artery disease from bilateral finger photoplethysmography sensing: A proof-of-concept study.
Biomedical Signal Processing and Control 86 (2023): 104993.
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Qiuke Wang, Jos Runhaar, Margreet Kloppenburg, Maarten Boers, Johannes WJ Bijlsma, Jaume Bacardit, Sita MA Bierma-Zeinstra
A machine learning approach reveals features related to clinicians¿ diagnosis of clinically relevant knee osteoarthritis
Rheumatology 62.8 (2023): 2732-2739
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Mylène P Jansen, Wolfgang Wirth, Jaume Bacardit, Eefje M van Helvoort, Anne CA Marijnissen, Margreet Kloppenburg, Francisco J Blanco, Ida K Haugen, Francis Berenbaum, Cristoph H Ladel, Marieke Loef, Floris PJG Lafeber, Paco M Welsing, Simon C Mastbergen, Frank W Roemer
Machine-learning predicted and actual 2-year structural progression in the IMI-APPROACH cohort
Quantitative Imaging in Medicine and Surgery 13.5 (2023): 3298.
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Christian Taylor, Jonathan Guy, Jaume Bacardit
Estimating individual-level pig growth trajectories from group-level weight time series using machine learning
Computers and Electronics in Agriculture, Volume 208, 2023,
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Yiming Huang, Nishant Sinha, Anil Wipat and Jaume Bacardit
A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis
Synthetic and Systems Biotechnology, Volume 8, Issue 1, March 2023, Pages 97-106
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Sokolovsky, A., Arnaboldi, L., Bacardit, J., and Gross, T.
Interpretable trading pattern designed for machine learning applications.
Machine Learning with Applications, Volume 11, 15 March 2023, 100448
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E.M. van Helvoort, M.P. Jansen, A.C.A. Marijnissen, M. Kloppenburg, F.J. Blanco, I.K Haugen, F. Berenbaum, A.C. Bay-Jensen, C. Ladel, A. Lalande, J. Larkin, J. Loughlin, A. Mobasheri, H.H. Weinans, P. Widera, J. Bacardit, P.M.J. Welsing and F.P.J.G. Lafeber
Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort
Rheumatology, Volume 62, Issue 1, January 2023, Pages 147-157
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W. Wirth, S. Maschek, A.C.A. Marijnissen, A. Lalande, F.J. Blanco, F. Berenbaum, L.A. van de Stadt, M. Kloppenburg, I.K. Haugen, C.H. Ladel, J. Bacardit, A. Wisser, F. Eckstein, F.W. Roemer, F.P.J.G. Lafeber, H.H. Weinans, M. Jansen
Test-retest precision and longitudinal cartilage thickness loss in the IMI-APPROACH cohort
Osteoarthritis and Cartilage 31 (2), 238-248
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2022

Frank W. Roemer, Mylène Jansen, Anne C. A. Marijnissen, Ali Guermazi, Rafael Heiss, Susanne Maschek, Agnes Lalande, Francisco J. Blanco, Francis Berenbaum, Lotte A. van de Stadt, Margreet Kloppenburg, Ida K. Haugen, Christoph H. Ladel, Jaume Bacardit, Anna Wisser, Felix Eckstein, Floris P. J. G. Lafeber, Harrie H. Weinans and Wolfgang Wirth
Structural tissue damage and 24-month progression of semi-quantitative MRI biomarkers of knee osteoarthritis in the IMI-APPROACH cohort
BMC Musculoskeletal Disorders volume 23, Article 988 (2022)
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Hajar Danesh; David H. Steel; Jeffry Hogg; Fereshteh Ashtari; Will Innes; Jaume Bacardit; Anya Hurlbert; Jenny C. A. Read; Rahele Kafieh
Synthetic OCT Data Generation to Enhance the Performance of Diagnostic Models for Neurodegenerative Diseases
Translational Vision Science & Technology October 2022, Vol.11, 10
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J. Bacardit, A.E.I. Brownlee, S. Cagnoni, G. Iacca, J. McCall, D. Walker
The intersection of evolutionary computation and explainable AI
Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp 1757-1762, 2022
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C. Taylor, J. Guy and J. Bacardit
Prediction of growth in grower-finisher pigs using recurrent neural networks
Biosystems engineering 2022 v.220 pp. 114-134
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F. Angelini, P. Widera, A. Mobasheri, J. Blair, A. Struglics, M. Uebelhoer, Y. Henrotin, A.C. Marijnissen, M. Kloppenburg, F. Blanco, I. Haugen, F. Berenbaum, C. Ladel, J. Larkin, A.C. Bay-Jensen and J. Bacardit
Osteoarthritis endotype discovery via clustering of biochemical marker data
Annals of the Rheumatic Diseases 81.5 (2022): 666-675.
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D Walker, M Ruane, J Bacardit, S Coleman
Insight from data analytics in a facilities management company
Quality and Reliability Engineering International 38.3 (2022): 1416-1440
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P. Darke, S. Cassidy, M. Catt, R. Taylor, P. Missier, and J. Bacardit
Curating a longitudinal research resource using linked primary care EHR data -- a UK Biobank case study
Journal of the American Medical Informatics Association 29.3 (2022): 546-552.
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2021

B. Little, O. Alshabrawy, D. Stow, I. N. Ferrier, R. McNaney, D. G. Jackson, K. Ladha, C. Ladha, T. Ploetz, J. Bacardit, P. Olivier, P. Gallagher and J. T. O'Brien
Deep learning-based automated speech detection as a marker of social functioning in late-life depression
Psychological medicine 51 (9), 1441-1450
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E.M. van Helvoort, C. Ladel, S. Mastbergen, M. Kloppenburg, F.J. Blanco, I.K. Haugen, F. Berenbaum, J. Bacardit, P. Widera, P.M.J. Welsing, F. Lafeber
Baseline clinical characteristics of predicted structural and pain progressors in the IMI-APPROACH knee OA cohort
RMD open 7 (3), e001759
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A.D. Rajgor, S. Patel, D. McCulloch, B. Obara, J. Bacardit, A. McQueen, E. Aboagye, T. Ali, J. O'Hara, D.W. Hamilton
The application of radiomics in laryngeal cancer
The British Journal of Radiology 94, 20210499
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L. Jardine, S. Webb, I. Goh, M. Quiroga Londoño, . Reynolds, M. Mather, B. Olabi, E. Stephenson, R.A. Botting, D. Horsfall, J. Engelbert,D. Maunder, N. Mende, C. Murnane, E. Dann, J. McGrath, H. King, I. Kucinski, R. Queen, C.D. Carey, C. Shrubsole, E. Poyner, M. Acres, C. Jones, T. Ness, R. Coulthard, N. Elliott, S. O'Byrne, M.LR. Haltalli, J.E. Lawrence, S. Lisgo, P. Balogh, K.B. Meyer, E. Prigmore, K. Ambridge, M.S. Jain, M. Efremova, K. Pickard, T. Creasey, J. Bacardit, D. Henderson, J. Coxhead, A. Filby, R. Hussain, D. Dixon, D. McDonald, D.M. Popescu, M.S. Kowalczyk, B. Li, O. Ashenberg, M. Tabaka, D. Dionne, T.L. Tickle, M. Slyper, O. Rozenblatt-Rosen, A. Regev, S. Behjati, E. Laurenti, N.K. Wilson, A. Roy, B. Göttgens, . Roberts, S.A. Teichmann, M. Haniffa
Blood and immune development in human fetal bone marrow and Down syndrome
Nature 598 (7880), 327-331
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E. Stephenson, G. Reynolds, R.A. Botting, F.J. Calero-Nieto, M.D. Morgan, Z.K. Tuong, K. Bach, W. Sungnak, K.B. Worlock, M. Yoshida, N. Kumasaka, K. Kania, J. Engelbert, B. Olabi, J.S. Spegarova, N.K. Wilson, N. Mende, L. Jardine, L.C.S. Gardner, I. Goh, D. Horsfall, J. McGrath, S. Webb, M.W. Mather, R.G.H. Lindeboom, E. Dann, N. Huang, K. Polanski, E. Prigmore, F. Gothe, J. Scott, R.P. Payne, K.F. Baker, A.T. Hanrath, I.C.D.S. van der Loeff, A.S. Barr, A. Sanchez-Gonzalez, L. Bergamaschi, F. Mescia, J.L. Barnes, E. Kilich, A. de Wilton, A. Saigal, A. Saleh, S.M. Janes, C.M. Smith, N. Gopee, C. Wilson, P. Coupland, J.M. Coxhead, V.Y. Kiselev, S. van Dongen, J. Bacardit, H.W. King, A.J. Rostron, A.J. Simpson, S. Hambleton, E. Laurenti, P.A. Lyons, K.B. Meyer, M.Z. Nikolic, C.J.A. Duncan, K.G.C. Smith, S.A. Teichmann, M.R. Clatworthy, J.C. Marioni, B. Göttgens, . Haniffa
Single-cell multi-omics analysis of the immune response in COVID-19
Nature medicine 27 (5), 904-916
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A.C. Hepburn, N. Lazzarini, R. Veeratterapillay, L. Wilson, J. Bacardit, R. Heer
Identification of CNGB1 as a Predictor of Response to Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer
Cancers 13 (15), 3903
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A. Sokolovsky, T. Gross, J. Bacardit
Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow
PloS one 16 (2), e0246464
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G. Reynolds, P. Vegh, J. Fletcher, E.F.M. Poyner, E. Stephenson, I. Goh, R.A. Botting, N. Huang, B. Olabi, A. Dubois, D. Dixon, K. Green, D. Maunder, J. Engelbert, M. Efremova, K. Polanski, L. Jardine, C. Jones, T. Ness, D. Horsfall, J. McGrath, C. Carey, D.M. Popescu, S. Webb, X.N. Wang, B. Sayer, J.E. Park, V.A. Negri, D. Belokhvostova, M.D. Lynch, D. McDonald, A. Filby, T. Hagai, K.B. Meyer, A. Husain, J. Coxhead, R. Vento-Tormo, S. Behjati, S. Lisgo, A.C. Villani, J. Bacardit, P.H. Jones, E.A. O'Toole, G.S. Ogg, N. Rajan, N.J. Reynolds, S.A. Teichmann, F.M. Watt, M. Haniffa
Developmental cell programs are co-opted in inflammatory skin disease
Science 371 (6527)
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Y. Huang, W. Smith, C. Harwood, A. Wipat and J. Bacardit
Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis
Sensors 2021, 21(7), 2436
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B. Lam, M. Catt, S. Cassidy, J. Bacardit, P. Darke, S. Butterfield, O. Alshabrawy, M. Trenell and P. Missier
Using wearable activity trackers to predict type 2 diabetes: machine learning–based cross-sectional study of the UK Biobank accelerometer cohort
JMIR diabetes 6.1 (2021): e23364.
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2020

A. Alameer, I. Kyriazakis and J. Bacardit
Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs
Scientific Reports volume 10, Article number: 13665 (2020)
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Eefje M van Helvoort, Willem E van Spil, Mylène P Janse1, Paco M J Welsing, Margreet Kloppenburgm, Marieke Loef, Francisco J Blanco, Ida K Haugen, Francis Berenbaum, Jaume Bacardit, Christoph H Ladel, John Loughlin, Anne C Bay-Jensen, Ali Mobasheri, Jonathan Larkin, Janneke Boere, Harrie H Weinans, Agnes Lalande, Anne C A Marijnissen and Floris P J G Lafeber
Cohort profile: The Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) study: a 2-year, European, cohort study to describe, validate and predict phenotypes of osteoarthritis using clinical, imaging and biochemical markers
BMJ Open 2020;10:e035101. doi: 10.1136/bmjopen-2019-035101
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Maria A. Franco, Natalio Krasnogor and Jaume Bacardit
Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure Identification
IEEE Computational Intelligence Magazine ( Volume: 15 , Issue: 3 , Aug. 2020 )
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Ali Alameer, Ilias Kyriazakis, Hillary A.Dalton, Amy L.Miller and Jaume Bacardit
Automatic recognition of feeding and foraging behaviour in pigs using deep learning
Biosystems Engineering, Volume 197, September 2020, Pages 91-104
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Paweł Widera, Paco M. J. Welsing, Christoph Ladel, John Loughlin, Floris P. F. J. Lafeber, Florence Petit Dop, Jonathan Larkin, Harrie Weinans, Ali Mobasheri and Jaume Bacardit
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data
Scientific Reports volume 10, Article number: 8427 (2020)
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2019

Dorin-Mirel Popescu, Rachel A. Botting, Emily Stephenson, Kile Green, Simone Webb, Laura Jardine, Emily F. Calderbank, Krzysztof Polanski, Issac Goh, Mirjana Efremova, Meghan Acres, Daniel Maunder, Peter Vegh, Yorick Gitton, Jong-Eun Park, Roser Vento-Tormo, Zhichao Miao, David Dixon, Rachel Rowell, David McDonald, James Fletcher, Elizabeth Poyner, Gary Reynolds, Michael Mather, Corina Moldovan, Lira Mamanova, Frankie Greig, Matthew D. Young, Kerstin B. Meyer, Steven Lisgo, Jaume Bacardit, Andrew Fuller, Ben Millar, Barbara Innes, Susan Lindsay, Michael J. T. Stubbington, Monika S. Kowalczyk, Bo Li, Orr Ashenberg, Marcin Tabaka, Danielle Dionne, Timothy L. Tickle, Michal Slyper, Orit Rozenblatt-Rosen, Andrew Filby, Peter Carey, Alexandra-Chloé Villani, Anindita Roy, Aviv Regev, Alain Chédotal, Irene Roberts, Berthold Göttgens, Sam Behjati, Elisa Laurenti, Sarah A. Teichmann and Muzlifah Haniffa
Decoding human fetal liver haematopoiesis
Nature 574:365-371 (2019)
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Wayne S. Smith, Shirley Coleman, Jaume Bacardit and Syd Coxon
Insight from data analytics with an automotive aftermarket SME
Quality and Reliability Engineering International 35 (5), 1396-1407
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Jake Cowton, Ilias Kyriazakis and Jaume Bacardit
Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning
IEEE Access, vol. 7, pp. 108049-108060, 2019
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Harold Fellermann, Alexandra S Penn, Rudolf M Füchslin, Jaume Bacardit, Angel Goñi-More
Towards low-carbon conferencing: Acceptance of virtual conferencing solutions and other sustainability measures in the alife community
The 2019 Conference on Artificial Life, Newcastle, United Kingdom, 29 July-2 August 2019, pp. 21-27
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2018

J Cowton, I Kyriazakis, T Ploetz, J Bacardit
A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors
Sensors 18(8):2521
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Keasar, C., McGuffin, L.J., Wallner, B., Chopra, G., Adhikari, B., Bhattacharya, D., Blake, L., Bortot, L. O., Cao, R., Dhanasekaran, B. K., Dimas, I., Faccioli, R.A., Faraggi, E., Ganzynkowicz, R., Ghosh, S., Ghosh, S., Gieldon A., Golon, L., He, Y., Heo, L., Hou, J., Khan, M., Khatib, F., Khoury, G.A., Kieslich, C., Kim, D.E., Krupa, P., Lee, G.R., Li, H., Li, J., Lipska, A., Liwo, A., Maghrabi, A.H.A., Mirdita, M., Mirzaei, S., Mozolewska, M.A., Onel, M., Ovchinnikov, S., Shah, A., Shah, U., Sidi, T., Sieradzan, A.K., Slusarz, M., Slusarz, R., Smadbeck, J., Tamamis, P., Trieber, N., Wirecki, T., Yin, Y., Zhang, Y., Bacardit, J., Baranowski, M., Chapman, N., Cooper, S., Defelicibus, A., Flatten, J., Koepnick, B., Popovic, Z., Zaborowski, B., Baker, D., Cheng, J., Czaplewski, C., Delbem, A.C.B., Floudas, C., Kloczkowski, A., Oldziej, S., Levitt, M., Scheraga, H., Seok, C., Soding, J., Vishveshwara, S., Xu, D., Crivelli, S.
An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12
Scientific Reports, 8(1), December 2018
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2017

N. Lazzarini, J. Runhaar, A.C. Bay-Jensen, C.S. Thudium, S.M.A.Bierma-Zeinstra, Y. Henrotin and J.Bacardit
A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women
Osteoarthritis and Cartilage, Volume 25, Issue 12, December 2017, Pages 2014-2021
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N. Lazzarini and J. Bacardit
RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers
BMC Bioinformatics 18:322, 2017
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S. Baron, N. Lazzarini and J. Bacardit
Characterising the Influence of Rule-Based Knowledge Representations in Biological Knowledge Extraction from Transcriptomics Data
Proceedings of EvoApplications 2017, LNCS 10199, p.125-141, Springer, 2017
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A Garcia-Piquer, J Bacardit, A Fornells and E Golobardes
Scaling-up multiobjective evolutionary clustering algorithms using stratification
Pattern Recognition Letters, 93:69-77, 2017
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2016

N. Lazzarini, P. Widera, S. Williamson, R. Heer, N. Krasnogor and J. Bacardit
Functional networks inference from rule-based machine learning models
BioData Mining 2016, 9:28
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P.D. Gutiérrez, M. Lastra, J. Bacardit, J.M. Benítez and F. Herrera
GPU-SME-kNN: Scalable and Memory Efficient kNN and Lazy Learning using GPUs
Information Sciences, 330:385-402
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M. Franco, and J. Bacardit
Large-scale experimental evaluation of GPU strategies for evolutionary machine learning
Information Sciences, 330:385-402
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2015

F. Eduati et al.
Prediction of human population responses to toxic compounds by a collaborative competition
Nature Biotechnology, 33,933-940 (2015)
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I. Triguero, S. del Río, V. López, J. Bacardit, J.M. Benítez and F. Herrera
ROSEFW-RF: The winner algorithm for the ECBDL'14 big data competition: An extremely imbalanced big data bioinformatics problem
Knowledge-Based Systems, (2015) 87:69-79
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A.L. Swan, D.J. Stekel, C. Hodgman, D. Allaway, M.H Alqahtani, A. Mobasheri and J. Bacardit
A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data
BMC Genomics 2015, 16(Suppl 1):S2
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I. Triguero, D. Peralta, J. Bacardit, S. Garcia and F. Herrera
MRPR: A MapReduce Solution for Prototype Reduction in Big Data Classification
Neurocomputing journal 150(A):331-345
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María Martínez-Ballesteros, Jaume Bacardit, Alicia Troncoso and José C. Riquelme
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets
Integrated Computer-Aided Engineering, 22(1):21-39
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2014

Alkurashi MM, May ST, Kong K, Bacardit J, Haig D, Elsheikha HM.
Susceptibility to experimental infection of the invertebrate locusts (Schistocerca gregaria) with the apicomplexan parasite Neospora caninum.
PeerJ 2:e674
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J. Bacardit, P. Widera, N. Lazzarini and N. Krasnogor
Hard Data Analytics Problems Make for Better Data Analysis Algorithms: Bioinformatics as an Example
Big Data Journal, 2(3):164-176, September 2014
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D.J. Gibbs, J. Bacardit, A. Bachmair, and M.J. Holdsworth
The eukaryotic N-end rule pathway: conserved mechanisms and diverse functions
Trends in Cell Biology, 24(10):603-611
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I. Triguero, D. Peralta, J. Bacardit, S. Garcia and F. Herrera
A Combined MapReduce-Windowing Two-Level Parallel Scheme for Evolutionary Prototype Generation
Proceedings of the IEEE World Congress on Computational Intelligence 2014, pp. 3036--3043
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J. Blakes, O. Raz, U. Feige, J. Bacardit, P. Widera, T. Ben-Yehezkel, E. Shapiro and N. Krasnogor
A heuristic for maximizing DNA reuse in synthetic DNA library assembly
ACS synthetic biology 3 (8), 529-542
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G.A Khoury, A. Liwo, F. Khatib, H. Zhou, G. Chopra, J. Bacardit, L. Bortot, R.A. Faccioli, X. Deng, Y. He, P. Krupa, J. Li, M.A. Mozolewska, A.K. Sieradzan, J. Smadbeck, T. Wirecki, S. Cooper, J. Flatten, K. Xu, D. Baker, J. Cheng, A.C.B. Delbem, C.A. Floudas, C. Keasar, M. Levitt, Z. Popovic, H.A. Scheraga, J. Skolnick, S.N. Crivelli and Foldit Players
WeFold: A Coopetition for Protein Structure Prediction
PROTEINS: Structure, Function, and Bioinformatics 82(9):1850-1868, 2014
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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, 18(1):36-53, 2014
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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
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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)
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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, 17(6):953-981, June 2013
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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
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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
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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
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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
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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
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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
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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.; Bernadó-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
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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
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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
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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
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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
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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
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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.
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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.; Bernadó-Mansilla, E.; Butz, M.V.; Kovacs, T.; Llorà, 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. 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

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 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


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 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



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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