Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
MPI: The Complete Reference
Using MPI-2: Advanced Features of the Message Passing Interface
Using MPI-2: Advanced Features of the Message Passing Interface
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Intelligent data analysis
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Classifier Conditions Using Gene Expression Programming
Learning Classifier Systems
Medical data mining: insights from winning two competitions
Data Mining and Knowledge Discovery
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
GEPCLASS: a classification rule discovery tool using gene expression programming
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Tracing significant association rules using critical least association rules model
International Journal of Innovative Computing and Applications
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A parallel rule induction system based on gene expression programming (GEP) is reported in this paper. The system was developed for data classification. The parallel processing environment was implemented on a cluster using a message-passing interface. A master-slave GEP was implemented according to the Michigan approach for representing a solution for a classification problem. A multiple master-slave system (islands) was implemented in order to observe the co-evolution effect. Experiments were done with ten datasets, and algorithms were systematically compared with C4.5. Results were analysed from the point of view of a multi-objective problem, taking into account both predictive accuracy and comprehensibility of induced rules. Overall results indicate that the proposed system achieves better predictive accuracy with shorter rules, when compared with C4.5.