Structure identification of fuzzy model
Fuzzy Sets and Systems
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Ensembling neural networks: many could be better than all
Artificial Intelligence
An Empirical Study of Multipopulation Genetic Programming
Genetic Programming and Evolvable Machines
The ``Test and Select'' Approach to Ensemble Combination
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Automatic Divide-and-Conquer Using Populations and Ensembles
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Genetic programming in classifying large-scale data: an ensemble method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Learning classifier system ensemble for data mining
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
An intelligent medical image understanding method using two-tier neural network ensembles
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Hierarchical two-tier ensemble learning: a new paradigm for network intrusion detection
Proceedings of the ACM first Ph.D. workshop in CIKM
An analysis of island models in evolutionary computation
An analysis of island models in evolutionary computation
Genotypic differences and migration policies in an island model
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Artificial Intelligence in Medicine
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Optimal ensemble construction via meta-evolutionary ensembles
Expert Systems with Applications: An International Journal
Perspectives of fuzzy systems and control
Fuzzy Sets and Systems
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
DASC '09 Proceedings of the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing
Ensemble techniques for parallel genetic programming based classifiers
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
On the impact of the migration topology on the Island Model
Parallel Computing
Efficient multi-objective higher order mutation testing with genetic programming
Journal of Systems and Software
Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning
IEEE Transactions on Knowledge and Data Engineering
A Generic Multilevel Architecture for Time Series Prediction
IEEE Transactions on Knowledge and Data Engineering
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
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We propose an evolutionary framework for the production of fuzzy rule bases where each rule executes an ensemble of predictors. The architecture, the rule base and the composition of the ensembles are evolved over time. To achieve this, we employ a context-free grammar within a hybrid genetic programming system using a multi-population model. As base predictors, multilayer perceptron neural networks and support vector machines are available. We apply the system to several function approximation and regression tasks and compare the results with recent research and state-of-the-art models. We conclude that the proposed architecture is competitive and has a number of very desirable features supporting automation of predictive model building and their adaptation over time. Finally, we suggest further potential research directions.