Jaume Bacardit

发表

Scaling-up multiobjective evolutionary clustering algorithms using stratification

Alvaro Garcia-Piquer, Jaume Bacardit, Albert Fornells, 2017, Pattern Recognit. Lett..

Experimental Evaluation of Discretization Schemes for Rule Induction

Federico Divina, Jesús S. Aguilar-Ruiz, Jaume Bacardit, 2004, GECCO.

Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets[C][W][OA]

Jaume Bacardit, George W Bassel, Enrico Glaab, 2011, Plant Cell.

t-SNE transformation: a normalization method for local features of single-cell RNA-seq data

Jaume Bacardit, An Chengrui, Zhang Nijia, 2019, bioRxiv.

MRPR: A MapReduce solution for prototype reduction in big data classification

Francisco Herrera, Salvador García, Jaume Bacardit, 2015, Neurocomputing.

GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning

Jaume Bacardit, Natalio Krasnogor, María A. Franco, 2013, Soft Comput..

Smart crossover operator with multiple parents for a Pittsburgh learning classifier system

Jaume Bacardit, Natalio Krasnogor, 2006, GECCO '06.

Post-processing operators for decision lists

Jaume Bacardit, Natalio Krasnogor, María A. Franco, 2012, GECCO '12.

Learning Classifier Systems: Looking Back and Glimpsing Ahead

Martin V. Butz, Jaume Bacardit, Ester Bernadó-Mansilla, 2008, IWLCS.

Large‐scale data mining using genetics‐based machine learning

Xavier Llorà, Jaume Bacardit, J. Bacardit, 2013, GECCO.

Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems

Jaume Bacardit, Natalio Krasnogor, 2009, Evolutionary Computation.

Large scale data mining using genetics-based machine learning

Xavier Llorà, Jaume Bacardit, J. Bacardit, 2009, GECCO '09.

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Jaume Bacardit, Ilias Kyriazakis, Ali Alameer, 2020, Scientific Reports.

ROSEFW-RF: The winner algorithm for the ECBDL'14 big data competition: An extremely imbalanced big data bioinformatics problem

Francisco Herrera, Jaume Bacardit, José Manuel Benítez, 2015, Knowl. Based Syst..

Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System

Jaume Bacardit, Josep Maria Garrell i Guiu, 2005, IWLCS.

Automated alphabet reduction method with evolutionary algorithms for protein structure prediction

Xavier Llorà, Kumara Sastry, Jaume Bacardit, 2007, GECCO '07.

Speeding up the evaluation of evolutionary learning systems using GPGPUs

Jaume Bacardit, Natalio Krasnogor, María A. Franco, 2010, GECCO '10.

Improving the scalability of rule-based evolutionary learning

Edmund K. Burke, Jaume Bacardit, Natalio Krasnogor, 2009, Memetic Comput..

Large-Scale Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering

Alvaro Garcia-Piquer, Jaume Bacardit, Albert Fornells, 2014, IEEE Transactions on Evolutionary Computation.

Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features

Federico Divina, Jesús S. Aguilar-Ruiz, Jaume Bacardit, 2012, Bioinform..

From HP Lattice Models to Real Proteins: Coordination Number Prediction Using Learning Classifier Systems

Jacek Blazewicz, Jaume Bacardit, Natalio Krasnogor, 2006, EvoWorkshops.

Sample classification and identification of biomarkers in the cartilage secretome from mass spectrometry datasets using machine learning

Jaume Bacardit, Ali Mobasheri, Anna L Swan, 2013 .

Characterising the Influence of Rule-Based Knowledge Representations in Biological Knowledge Extraction from Transcriptomics Data

Jaume Bacardit, Simon Baron, Nicola Lazzarini, 2017, EvoApplications.

Data Mining in Learning Classifier Systems: Comparing XCS with GAssist

Martin V. Butz, Jaume Bacardit, 2005, IWLCS.

Data harmonisation across databases for machine learning model development to predict knee osteoarthritis progression

Jaume Bacardit, Ali Mobasheri, Harrie Weinans, 2018 .

Data Mining in Proteomics with Learning Classifier Systems

Jaume Bacardit, Natalio Krasnogor, Jonathan D. Hirst, 2008, Learning Classifier Systems in Data Mining.

Coordination number prediction using learning classifier systems: performance and interpretability

Jacek Blazewicz, Jaume Bacardit, Natalio Krasnogor, 2006, GECCO '06.

Artificial Life in a Challenged World

Jaume Bacardit, Rudolf Marcel Füchslin, Ángel Goñi Moreno, 2019, Artificial Life Conference Proceedings.

Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets

Alicia Troncoso Lora, José Cristóbal Riquelme Santos, Jaume Bacardit, 2015, Integr. Comput. Aided Eng..

Learning Classifier Systems: 11th International Workshop, IWLCS 2008, Atlanta, GA, USA, July 13, 2008, and 12th International Workshop, IWLCS 2009, ... / Lecture Notes in Artificial Intelligence)

Martin V. Butz, Jaume Bacardit, Jan Drugowitsch, 2011 .

A combined MapReduce-windowing two-level parallel scheme for evolutionary prototype generation

Francisco Herrera, Salvador García, Jaume Bacardit, 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

The Role of Interval Initialization in a GBML System with Rule Representation and Adaptive Discrete Intervals

Jaume Bacardit, Josep Maria Garrell i Guiu, 2002, CCIA.

Session details: Workshop: evolutionary computation for big data and big learning

Kalyan Veeramachaneni, Jaume Bacardit, Una-May O'Reilly, 2014, GECCO 2014.

Large scale data mining using genetics-based machine learning

Xavier Llorà, Jaume Bacardit, J. Bacardit, 2011, GECCO.

A tale of human-competitiveness in bioinformatics

Jaume Bacardit, Natalio Krasnogor, Michael Stout, 2008, SEVO.

The 2019 Conference on Artificial Life

Jaume Bacardit, Rudolf Marcel Füchslin, Harold Fellermann, 2019, The 2019 Conference on Artificial Life.

How data can change the automotive aftermarket

Shirley Coleman, Jaume Bacardit, Wayne S. Smith, 2018 .

Automatic recognition of feeding and foraging behaviour in pigs using deep learning

Jaume Bacardit, Ilias Kyriazakis, Ali Alameer, 2020, Biosystems Engineering.

Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule

Martin V. Butz, David E. Goldberg, Jaume Bacardit, 2005, IWLCS.

KEEL: a software tool to assess evolutionary algorithms for data mining problems

María José del Jesús, Jesús Alcalá-Fdez, Francisco Herrera, 2008, Soft Comput..

Decoding human fetal liver haematopoiesis

David McDonald, Kerstin B. Meyer, Jaume Bacardit, 2019, Nature.

Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure Identification

Jaume Bacardit, Natalio Krasnogor, Maria A. Franco, 2020, IEEE Computational Intelligence Magazine.

Using Rule-Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data

Jonathan M. Garibaldi, Jaume Bacardit, Natalio Krasnogor, 2012, PloS one.

A NSGA-II Algorithm for the Residue-Residue Contact Prediction

Federico Divina, Jesús S. Aguilar-Ruiz, Jaume Bacardit, 2012, EvoBIO.

Learning classifier systems for optimisation problems: a case study on fractal travelling salesman problem

Jaume Bacardit, Natalio Krasnogor, Irene Loiseau, 2008, GECCO '08.

Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology

Jaume Bacardit, Natalio Krasnogor, Carlos García-Martínez, 2010, GECCO '10.

Evolving Multiple Discretizations with Adaptive Intervals for a Pittsburgh Rule-Based Learning Classifier System

Jaume Bacardit, Josep Maria Garrell i Guiu, 2003, GECCO.

Prediction of recursive convex hull class assignments for protein residues

Jaume Bacardit, Natalio Krasnogor, Jonathan D. Hirst, 2008, Bioinform..

A learning classifier system with mutual-information-based fitness

Jaume Bacardit, Natalio Krasnogor, Jonathan D. Hirst, 2007, 2007 IEEE Congress on Evolutionary Computation.

Hard Data Analytics Problems Make for Better Data Analysis Algorithms: Bioinformatics as an Example

Jaume Bacardit, Natalio Krasnogor, Paweł Widera, 2014, Big Data.

Evolution Of Adaptive Discretization Intervals For A Rule-based Genetic Learning System

Jaume Bacardit, Josep Maria Garrell i Guiu, 2002, GECCO.

A mixed discrete-continuous attribute list representation for large scale classification domains

Jaume Bacardit, Natalio Krasnogor, 2009, GECCO '09.

Analysis and Improvements of the Adaptive Discretization Intervals Knowledge Representation

Jaume Bacardit, Josep Maria Garrell i Guiu, 2004, GECCO.

Automated Alphabet Reduction for Protein Datasets

Alfonso Valencia, Jaume Bacardit, Natalio Krasnogor, 2009, BMC Bioinformatics.

RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers

Jaume Bacardit, Nicola Lazzarini, J. Bacardit, 2017, BMC Bioinformatics.

WeFold: A coopetition for protein structure prediction

Adam Liwo, Yi He, David Baker, 2014, Proteins.

Large-scale experimental evaluation of GPU strategies for evolutionary machine learning

Jaume Bacardit, María A. Franco, 2016, Inf. Sci..

Speeding-Up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy

Martin V. Butz, Xavier Llorà, David E. Goldberg, 2004, PPSN.

Alife 2019 : proceedings of the Artificial Life Conference 2019

Jaume Bacardit, Rudolf Marcel Füchslin, Harold Fellermann, 2019 .

Development of a Genetic Algorithm to Design and Identify Peptides that can Cross the Blood-Brain Barrier

Xavier Llorà, Fernando Albericio, Jaume Bacardit, 2003 .

HARD DATA ANALYTICS PROBLEMS MAKE FOR BETTER DATA ANALYSIS ALGORITHMS: as an Example

Jaume Bacardit, Natalio Krasnogor, Nicola Lazzarini, 2014 .

BioHEL: Bioinformatics-oriented Hierarchical Evolutionary Learning

Jaume Bacardit, Natalio Krasnogor, J. Bacardit, 2006 .

Towards Low-Carbon Conferencing: Acceptance of Virtual Conferencing Solutions and Other Sustainability Measures in the ALIFE Community

Jaume Bacardit, Alexandra S. Penn, Rudolf Marcel Füchslin, 2019, Artificial Life Conference Proceedings.

Prediction of topological contacts in proteins using learning classifier systems

Jaume Bacardit, Natalio Krasnogor, Jonathan D. Hirst, 2008, Soft Comput..

Modelling the initialisation stage of the ALKR representation for discrete domains and GABIL encoding

Jaume Bacardit, Natalio Krasnogor, María A. Franco, 2011, GECCO '11.

Applications of evolutionary computation: 19th European conference, Evoapplications 2016 Porto, Portugal, March 30 – April 1, 2016 proceedings, part II

Jaume Bacardit, 2016 .

Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System

Jaume Bacardit, Natalio Krasnogor, 2007, IWLCS.

Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning

Jaume Bacardit, Ilias Kyriazakis, Jake Cowton, 2019, IEEE Access.

Insight from data analytics with an automotive aftermarket SME

Shirley Coleman, Jaume Bacardit, Wayne S. Smith, 2019, Qual. Reliab. Eng. Int..

An efficient decision rule-based system for the protein residue-residue contact prediction

Jesús S. Aguilar-Ruiz, Jaume Bacardit, Federico Divina, 2013, 2013 IEEE Congress on Evolutionary Computation.

Guest Editorial: Thematic Issue on ‘Metaheuristics for large scale data mining’

Xavier Llorà, Jaume Bacardit, J. Bacardit, 2010, Memetic Comput..

Integrating memetic search into the BioHEL evolutionary learning system for large-scale datasets

Jaume Bacardit, Dan Andrei Calian, 2013, Memetic Comput..

A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors

Jaume Bacardit, Ilias Kyriazakis, Thomas Plötz, 2018, Sensors.

Analysing BioHEL using challenging boolean functions

Jaume Bacardit, Natalio Krasnogor, María A. Franco, 2012, Evol. Intell..

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Adam Liwo, Yi He, Jilong Li, 2018, Scientific Reports.

Functional networks inference from rule-based machine learning models

Jaume Bacardit, Natalio Krasnogor, Pawel Widera, 2016, BioData Mining.

GPU-SME-kNN: Scalable and memory efficient kNN and lazy learning using GPUs

Francisco Herrera, Miguel Lastra, Jaume Bacardit, 2016, Inf. Sci..

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data

Jaume Bacardit, Pawel Widera, Harrie Weinans, 2020, Scientific Reports.

Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System

Jaume Bacardit, Josep Maria Garrell i Guiu, 2002, IBERAMIA.

Analysing bioHEL using challenging boolean functions

Jaume Bacardit, Natalio Krasnogor, María A. Franco, 2010, GECCO '10.

Fast rule representation for continuous attributes in genetics-based machine learning

Jaume Bacardit, Natalio Krasnogor, 2008, GECCO '08.

Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States in Bacillus subtilis

Jaume Bacardit, Anil Wipat, Wendy Smith, 2021, Sensors.

Interpretable ML-driven Strategy for Automated Trading Pattern Extraction pdf

Jaume Bacardit, Thomas Gross, Luca Arnaboldi, 2021, ArXiv.

Insight from data analytics in a facilities management company

Jaume Bacardit, Shirley Coleman, Daniel Walker, 2021, Qual. Reliab. Eng. Int..

Explainable Machine Learning-driven Strategy for Automated Trading Pattern Extraction A Case Study of CME Futures

Jaume Bacardit, Thomas Gross, Luca Arnaboldi, 2021 .

Heuristic for maximizing DNA reuse in synthetic DNA library assembly.

Uriel Feige, Jaume Bacardit, Natalio Krasnogor, 2014, ACS synthetic biology.

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

Martin V. Butz, Xavier Llorà, Tim Kovacs, 2008, IWLCS.

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

Jaume Bacardit, Iwlcs, 2010 .

Comparison of training set reduction techniques for Pittsburgh approach Genetic Classifier Systems

Jaume Bacardit, Josep Maria Garrell, 2003 .

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

Jaume Bacardit, Ali Mobasheri, Anna L Swan, 2013, BMC Musculoskeletal Disorders.

PREDICTION OF RESIDUE EXPOSURE AND CONTACT NUMBER FOR SIMPLIFIED HP LATTICE MODEL PROTEINS USING LEARNING CLASSIFIER SYSTEMS

Jacek Blazewicz, Jaume Bacardit, Natalio Krasnogor, 2006 .

Reduced Neonatal Mortality in Meishan Piglets: A Role for Hepatic Fatty Acids?

Jaume Bacardit, Michael E. Symonds, Frank Wessely, 2012, PloS one.

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Jung-Hsien Chiang, Devin C. Koestler, Celine Vens, 2018, bioRxiv.

Decoding the development of the blood and immune systems during human fetal liver haematopoiesis

Jaume Bacardit, Sarah A. Teichmann, Berthold Göttgens, 2019, bioRxiv.