Bacardit, Jaume - School of Computer Science, University of Nottingham

• G53BIO Coursework PART 2: Project Report
• Combinando multiples discretizadores para aprendizaje de reglas evolutivo con enfoque
• Experimental Evaluation of Discretization Schemes for Rule Induction
• Incremental Learning for Pittsburgh Approach Classi er Systems
• Analysis of the Initialization Stage of a Pittsburgh Approach Learning Classifier System
• Bloat control and generalization pressure using the minimum description length principle for a
• The University of Nottingham SCHOOL OF COMPUTER SCIENCE AND INFORMATION
• Data Mining in Learning Classifier Systems: Comparing XCS with GAssist
• April 7, 2006 15:15 Proceedings Trim Size: 9in x 6in CIBB2006-ms PREDICTION OF RESIDUE EXPOSURE AND CONTACT
• Pittsburgh Genetic-Based Machine Learning in the Data Mining era: Representations,
• A Mixed Discrete-Continuous Attribute List Representation for Large Scale Classification Domains
• Fast Rule Representation for Continuous Attributes in Genetics-Based Machine Learning
• Data Mining in Proteomics with Learning Classifier Systems
• Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule
• Bloat control and generalization pressure using the minimum description length principle for a
• Automated Alphabet Reduction Method with Evolutionary Algorithms for Protein Structure Prediction
• Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System
• From HP Lattice Models to Real Proteins: Coordination Number Prediction Using
• G53BIO Coursework PART 1: Survey Report
• G54DMT Project Submission guidelines and deadlines
• School of Computer Science and Information Technology Computer Systems Architecture (G51CSA) Autumn 2009
• School of Computer Science and Information Technology Computer Systems Architecture (G51CSA) Autumn 2009
• School of Computer Science and Information Technology Computer Systems Architecture (G51CSA) Autumn 2009
• Memetic Computing manuscript No. (will be inserted by the editor)
• Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System
• Data Mining in Learning Classifier Systems: Comparing XCS with GAssist
• Jaume Bacardit Curriculum Vitae
• Memetic Computing manuscript No. (will be inserted by the editor)
• Comparison of training set reduction techniques for Pittsburgh approach Genetic Classifier
• Evolving multiple discretizations with adaptive intervals for a Pittsburgh RuleBased Learning
• Learning Classifier Systems for Optimisation Problems: A Case Study on Fractal Travelling Salesman Problem
• Smart Crossover Operator with Multiple Parents for a Pittsburgh Learning Classifier System
• School of Computer Science and Information Technology Computer Systems Architecture (G51CSA) Autumn 2009
• The role of interval initialization in a GBML system with rule representation and adaptive
• Improving the Performance of a Pittsburgh Learning Classifier
• Learning Classifier Systems: Looking Back and Glimpsing Ahead
• Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, 20 July 1997, East Lansing, Michigan, USA
• Coordination Number Prediction Using Learning Classifier Systems: Performance and interpretability
• Evolution of multi-adaptive discretization intervals for a rule-based genetic learning system
• Speeding-up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy
• Analysis and improvements of the Adaptive Discretization Intervals knowledge
• Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems