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2020 – today
- 2024
[j34]Jaume Bacardit
, Alexander E. I. Brownlee, Stefano Cagnoni, Giovanni Iacca, John A. W. McCall, David J. Walker:
Introduction to the Special Issue on Explainable AI in Evolutionary Computation. ACM Trans. Evol. Learn. Optim. 4(1): 1:1-1:2 (2024)- [i5]Ryan Zhou, Jaume Bacardit, Alexander E. I. Brownlee, Stefano Cagnoni, Martin Fyvie, Giovanni Iacca, John A. W. McCall, Niki van Stein, David Walker, Ting Hu:
Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems. CoRR abs/2406.07811 (2024) - 2023
- [j33]Sadaf Iqbal, Sharad Agarwal, Ian Purcell, Alan Murray, Jaume Bacardit, John Allen:
Deep learning identification of coronary artery disease from bilateral finger photoplethysmography sensing: A proof-of-concept study. Biomed. Signal Process. Control. 86(Part A): 104993 (2023) - [j32]Christian Taylor, Jonathan Guy, Jaume Bacardit:
Estimating individual-level pig growth trajectories from group-level weight time series using machine learning. Comput. Electron. Agric. 208: 107790 (2023) - 2022
- [j31]Philip Darke, Sophie A. Cassidy, Michael Catt, Roy Taylor, Paolo Missier, Jaume Bacardit:
Curating a longitudinal research resource using linked primary care EHR data - a UK Biobank case study. J. Am. Medical Informatics Assoc. 29(3): 546-552 (2022) - [j30]Daniel Walker, Martin Ruane, Jaume Bacardit, Shirley Coleman:
Insight from data analytics in a facilities management company. Qual. Reliab. Eng. Int. 38(3): 1416-1440 (2022) - [c36]Jaume Bacardit, Alexander E. I. Brownlee, Stefano Cagnoni, Giovanni Iacca, John A. W:
McCall, David Walker: The intersection of evolutionary computation and explainable AI. GECCO Companion 2022: 1757-1762 - [i4]Philip Darke, Paolo Missier, Jaume Bacardit:
Benchmark time series data sets for PyTorch - the torchtime package. CoRR abs/2207.12503 (2022) - 2021
- [j29]Yiming Huang, Wendy Smith, Colin Harwood, Anil Wipat, Jaume Bacardit:
Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis. Sensors 21(7): 2436 (2021) - [i3]Artur Sokolovsky, Luca Arnaboldi, Jaume Bacardit, Thomas Gross:
Interpretable ML-driven Strategy for Automated Trading Pattern Extraction. CoRR abs/2103.12419 (2021) - 2020
- [j28]María A. Franco, Natalio Krasnogor, Jaume Bacardit:
Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure Identification. IEEE Comput. Intell. Mag. 15(3): 28-46 (2020) - [i2]Artur Sokolovsky, Thomas Gross, Jaume Bacardit:
Detection of FLOSS version release events from Stack Overflow message data. CoRR abs/2003.14257 (2020)
2010 – 2019
- 2019
- [j27]Jake Cowton, Ilias Kyriazakis, Jaume Bacardit:
Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning. IEEE Access 7: 108049-108060 (2019) - [j26]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, Muzlifah Haniffa:
Decoding human fetal liver haematopoiesis. Nat. 574(7778): 365-371 (2019) - [j25]Wayne S. Smith, Shirley Coleman, Jaume Bacardit, Syd Coxon:
Insight from data analytics with an automotive aftermarket SME. Qual. Reliab. Eng. Int. 35(5): 1396-1407 (2019) - [c35]Harold Fellermann, Jaume Bacardit, Ángel Goñi-Moreno, Rudolf M. Füchslin:
Artificial Life in a Challenged World. ALIFE 2019: 1-3 - [c34]Harold Fellermann, Alexandra S. Penn, Rudolf M. Füchslin, Jaume Bacardit, Ángel Goñi-Moreno:
Towards Low-Carbon Conferencing: Acceptance of Virtual Conferencing Solutions and Other Sustainability Measures in the ALIFE Community. ALIFE 2019: 21-27 - [e3]Harold Fellermann, Jaume Bacardit, Ángel Goñi-Moreno, Rudolf M. Füchslin:
2019 Conference on Artificial Life, ALIFE 2019, online, July 29 - August 2, 2019. MIT Press 2019 [contents] - [i1]Pawel Widera, Paco M. J. Welsing, Christoph Ladel, John Loughlin, Floris P. F. J. Lafeber, Florence Petit Dop, Jonathan Larkin, Harrie Weinans, Ali Mobasheri, Jaume Bacardit:
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data. CoRR abs/1909.13408 (2019) - 2018
- [j24]Jake Cowton, Ilias Kyriazakis, Thomas Plötz, Jaume Bacardit:
A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors. Sensors 18(8): 2521 (2018) - 2017
- [j23]Nicola Lazzarini, Jaume Bacardit:
RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers. BMC Bioinform. 18(1): 322 (2017) - [j22]Alvaro Garcia-Piquer, Jaume Bacardit, Albert Fornells, Elisabet Golobardes:
Scaling-up multiobjective evolutionary clustering algorithms using stratification. Pattern Recognit. Lett. 93: 69-77 (2017) - [c33]Simon Baron, Nicola Lazzarini, Jaume Bacardit:
Characterising the Influence of Rule-Based Knowledge Representations in Biological Knowledge Extraction from Transcriptomics Data. EvoApplications (1) 2017: 125-141 - 2016
- [j21]Nicola Lazzarini, Pawel Widera, Stuart Williamson, Rakesh Heer, Natalio Krasnogor, Jaume Bacardit:
Functional networks inference from rule-based machine learning models. BioData Min. 9: 28 (2016) - [j20]María A. Franco, Jaume Bacardit:
Large-scale experimental evaluation of GPU strategies for evolutionary machine learning. Inf. Sci. 330: 385-402 (2016) - [j19]Pablo David Gutiérrez, Miguel Lastra, Jaume Bacardit, José Manuel Benítez, Francisco Herrera:
GPU-SME-kNN: Scalable and memory efficient kNN and lazy learning using GPUs. Inf. Sci. 373: 165-182 (2016) - 2015
- [j18]María Martínez-Ballesteros, Jaume Bacardit, Alicia Troncoso Lora, José C. Riquelme:
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets. Integr. Comput. Aided Eng. 22(1): 21-39 (2015) - [j17]Isaac Triguero, Daniel Peralta, Jaume Bacardit, Salvador García, Francisco Herrera:
MRPR: A MapReduce solution for prototype reduction in big data classification. Neurocomputing 150: 331-345 (2015) - [j16]Isaac Triguero, Sara del Río, Victoria López, Jaume Bacardit, José Manuel Benítez, Francisco Herrera:
ROSEFW-RF: The winner algorithm for the ECBDL'14 big data competition: An extremely imbalanced big data bioinformatics problem. Knowl. Based Syst. 87: 69-79 (2015) - 2014
- [j15]Jaume Bacardit, Pawel Widera, Nicola Lazzarini, Natalio Krasnogor:
Hard Data Analytics Problems Make for Better Data Analysis Algorithms: Bioinformatics as an Example. Big Data 2(3): 164-176 (2014) - [j14]Alvaro Garcia-Piquer, Albert Fornells, Jaume Bacardit, Albert Orriols-Puig, Elisabet Golobardes:
Large-Scale Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering. IEEE Trans. Evol. Comput. 18(1): 36-53 (2014) - [c32]Isaac Triguero, Daniel Peralta, Jaume Bacardit, Salvador García, Francisco Herrera:
A combined MapReduce-windowing two-level parallel scheme for evolutionary prototype generation. IEEE Congress on Evolutionary Computation 2014: 3036-3043 - 2013
- [j13]Dan Andrei Calian, Jaume Bacardit:
Integrating memetic search into the BioHEL evolutionary learning system for large-scale datasets. Memetic Comput. 5(2): 95-130 (2013) - [j12]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) - [j11]Jaume Bacardit, Xavier Llorà:
Large-scale data mining using genetics-based machine learning. WIREs Data Mining Knowl. Discov. 3(1): 37-61 (2013) - [c31]Alfonso E. Márquez Chamorro, Federico Divina, Jaume Bacardit, Jesús S. Aguilar-Ruiz:
An efficient decision rule-based system for the protein residue-residue contact prediction. IEEE Congress on Evolutionary Computation 2013: 592-597 - [c30]Jaume Bacardit, Xavier Llorà:
Large scale data mining using genetics-based machine learning. GECCO (Companion) 2013: 741-764 - 2012
- [j10]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. Bioinform. 28(19): 2441-2448 (2012) - [j9]María A. Franco, Natalio Krasnogor, Jaume Bacardit:
Analysing BioHEL using challenging boolean functions. Evol. Intell. 5(2): 87-102 (2012) - [c29]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 - [c28]María A. Franco, Natalio Krasnogor, Jaume Bacardit:
Post-processing operators for decision lists. GECCO 2012: 847-854 - [c27]Jaume Bacardit, Xavier Llorà:
Large scale data mining using genetics-based machine learning. GECCO (Companion) 2012: 1171-1196 - 2011
- [c26]Jaume Bacardit, Xavier Llorà:
Large scale data mining using genetics-based machine learning. GECCO (Companion) 2011: 1285-1310 - [c25]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 - 2010
- [j8]Robert Elliott Smith, Max Kun Jiang, Jaume Bacardit, Michael Stout, Natalio Krasnogor, Jonathan D. Hirst:
A learning classifier system with mutual-information-based fitness. Evol. Intell. 3(1): 31-50 (2010) - [j7]Jaume Bacardit, Xavier Llorà:
Guest Editorial: Thematic Issue on 'Metaheuristics for large scale data mining'. Memetic Comput. 2(3): 163-164 (2010) - [c24]María A. Franco, Natalio Krasnogor, Jaume Bacardit:
Speeding up the evaluation of evolutionary learning systems using GPGPUs. GECCO 2010: 1039-1046 - [c23]María A. Franco, Natalio Krasnogor, Jaume Bacardit:
Analysing bioHEL using challenging boolean functions. GECCO (Companion) 2010: 1855-1862 - [c22]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 - [e2]Jaume Bacardit, Will N. Browne, Jan Drugowitsch, Ester Bernadó-Mansilla, Martin V. Butz:
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 [contents]
2000 – 2009
- 2009
- [j6]Jaume Bacardit, Michael Stout, Jonathan D. Hirst, Alfonso Valencia, Robert Elliott Smith, Natalio Krasnogor:
Automated Alphabet Reduction for Protein Datasets. BMC Bioinform. 10 (2009) - [j5]Jaume Bacardit, Natalio Krasnogor:
Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems. Evol. Comput. 17(3): 307-342 (2009) - [j4]Jaume Bacardit, Edmund K. Burke, Natalio Krasnogor:
Improving the scalability of rule-based evolutionary learning. Memetic Comput. 1(1): 55-67 (2009) - [j3]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) - [j2]Jesús Alcalá-Fdez, Luciano Sánchez, Salvador García, María José del Jesus, 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) - [c21]Jaume Bacardit, Natalio Krasnogor:
A mixed discrete-continuous attribute list representation for large scale classification domains. GECCO 2009: 1155-1162 - [c20]Jaume Bacardit, Xavier Llorà:
Large scale data mining using genetics-based machine learning. GECCO (Companion) 2009: 3381-3412 - 2008
- [j1]Michael Stout, Jaume Bacardit, Jonathan D. Hirst, Natalio Krasnogor:
Prediction of recursive convex hull class assignments for protein residues. Bioinform. 24(7): 916-923 (2008) - [c19]Jaume Bacardit, Natalio Krasnogor:
Fast rule representation for continuous attributes in genetics-based machine learning. GECCO 2008: 1421-1422 - [c18]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 - [p1]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 - [e1]Jaume Bacardit, Ester Bernadó-Mansilla, Martin V. Butz, Tim Kovacs, Xavier Llorà, Keiki Takadama:
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 [contents] - 2007
- [c17]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 - [c16]Jaume Bacardit, Ester Bernadó-Mansilla, Martin V. Butz:
Learning Classifier Systems: Looking Back and Glimpsing Ahead. IWLCS 2007: 1-21 - [c15]Jaume Bacardit, Natalio Krasnogor:
Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System. IWLCS 2007: 255-268 - 2006
- [c14]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 - [c13]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 - [c12]Jaume Bacardit, Natalio Krasnogor:
Smart crossover operator with multiple parents for a Pittsburgh learning classifier system. GECCO 2006: 1441-1448 - 2005
- [c11]Jaume Bacardit:
Analysis of the initialization stage of a Pittsburgh approach learning classifier system. GECCO 2005: 1843-1850 - [c10]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 - [c9]Jaume Bacardit, Martin V. Butz:
Data Mining in Learning Classifier Systems: Comparing XCS with GAssist. IWLCS 2005: 282-290 - [c8]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 - 2004
- [c7]Jaume Bacardit, Josep Maria Garrell i Guiu:
Analysis and Improvements of the Adaptive Discretization Intervals Knowledge Representation. GECCO (2) 2004: 726-738 - [c6]Jesús S. Aguilar-Ruiz, Jaume Bacardit, Federico Divina:
Experimental Evaluation of Discretization Schemes for Rule Induction. GECCO (1) 2004: 828-839 - [c5]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 - 2003
- [c4]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 - 2002
- [c3]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 - [c2]Jaume Bacardit, Josep Maria Garrell i Guiu:
Evolution Of Adaptive Discretization Intervals For A Rule-based Genetic Learning System. GECCO 2002: 677 - [c1]Jaume Bacardit, Josep Maria Garrell i Guiu:
Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System. IBERAMIA 2002: 350-360
Coauthor Index
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