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Publications of Author

1. Jaume Bacardit, Josep Maria Garrell i Guiu
   The Role of Interval Initialization in a GBML System with Rule Representation and Adaptive Discrete Intervals. [Citation Graph (0, 0)][DBLP]
   CCIA, 2002, pp:184-195 [Conf]
   
2. 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. [Citation Graph (0, 0)][DBLP]
   EvoWorkshops, 2006, pp:208-220 [Conf]
   
3. Jaume Bacardit
   Analysis of the initialization stage of a Pittsburgh approach learning classifier system. [Citation Graph (0, 0)][DBLP]
   GECCO, 2005, pp:1843-1850 [Conf]
   
4. Jaume Bacardit, Josep Maria Garrell i Guiu
   Evolution Of Adaptive Discretization Intervals For A Rule-based Genetic Learning System. [Citation Graph (0, 0)][DBLP]
   GECCO, 2002, pp:677- [Conf]
   
5. Jaume Bacardit, Josep Maria Garrell i Guiu
   Evolving Multiple Discretizations with Adaptive Intervals for a Pittsburgh Rule-Based Learning Classifier System. [Citation Graph (0, 0)][DBLP]
   GECCO, 2003, pp:1818-1831 [Conf]
   
6. Jaume Bacardit, Josep Maria Garrell i Guiu
   Analysis and Improvements of the Adaptive Discretization Intervals Knowledge Representation. [Citation Graph (0, 0)][DBLP]
   GECCO (2), 2004, pp:726-738 [Conf]
   
7. Jaume Bacardit, Natalio Krasnogor
   Smart crossover operator with multiple parents for a Pittsburgh learning classifier system. [Citation Graph (0, 0)][DBLP]
   GECCO, 2006, pp:1441-1448 [Conf]
   
8. Jaume Bacardit, Michael Stout, Natalio Krasnogor, Jonathan D. Hirst, Jacek Blazewicz
   Coordination number prediction using learning classifier systems: performance and interpretability. [Citation Graph (0, 0)][DBLP]
   GECCO, 2006, pp:247-254 [Conf]
   
9. Jesús S. Aguilar-Ruiz, Jaume Bacardit, Federico Divina
   Experimental Evaluation of Discretization Schemes for Rule Induction. [Citation Graph (0, 0)][DBLP]
   GECCO (1), 2004, pp:828-839 [Conf]
   
10. Jaume Bacardit, Josep Maria Garrell i Guiu
    Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System. [Citation Graph (0, 0)][DBLP]
    IBERAMIA, 2002, pp:350-360 [Conf]
    
11. 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. [Citation Graph (0, 0)][DBLP]
    PPSN, 2004, pp:1021-1031 [Conf]
    
12. Jaume Bacardit, Michael Stout, Jonathan D. Hirst, Kumara Sastry, Xavier Llorà, Natalio Krasnogor
    Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. [Citation Graph (0, 0)][DBLP]
    GECCO, 2007, pp:346-353 [Conf]
    
13. Jaume Bacardit, Martin V. Butz
    Data Mining in Learning Classifier Systems: Comparing XCS with GAssist. [Citation Graph (0, 0)][DBLP]
    IWLCS, 2005, pp:282-290 [Conf]
    
14. Jaume Bacardit, Josep Maria Garrell
    Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System. [Citation Graph (0, 0)][DBLP]
    IWLCS, 2005, pp:59-79 [Conf]
    
15. Jaume Bacardit, David E. Goldberg, Martin V. Butz
    Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule. [Citation Graph (0, 0)][DBLP]
    IWLCS, 2005, pp:291-307 [Conf]
    
16. 
    Fast rule representation for continuous attributes in genetics-based machine learning. [Citation Graph (, )][DBLP]
    
    
17. 
    Learning classifier systems for optimisation problems: a case study on fractal travelling salesman problem. [Citation Graph (, )][DBLP]
    
    
18. 
    A mixed discrete-continuous attribute list representation for large scale classification domains. [Citation Graph (, )][DBLP]
    
    
19. 
    Large scale data mining using genetics-based machine learning. [Citation Graph (, )][DBLP]
    
    
20. 
    Speeding up the evaluation of evolutionary learning systems using GPGPUs. [Citation Graph (, )][DBLP]
    
    
21. 
    Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology. [Citation Graph (, )][DBLP]
    
    
22. 
    Analysing bioHEL using challenging boolean functions. [Citation Graph (, )][DBLP]
    
    
23. 
    Learning Classifier Systems: Looking Back and Glimpsing Ahead. [Citation Graph (, )][DBLP]
    
    
24. 
    Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System. [Citation Graph (, )][DBLP]
    
    
25. 
    Prediction of recursive convex hull class assignments for protein residues. [Citation Graph (, )][DBLP]
    
    
26. 
    Automated Alphabet Reduction for Protein Datasets. [Citation Graph (, )][DBLP]
    
    
27. 
    Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems. [Citation Graph (, )][DBLP]