CLONAL-GP framework for artificial immune system inspired genetic programming for classification
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
A niched genetic programming algorithm for classification rules discovery in geographic databases
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Evolutionary multi objective optimization for rule mining: a review
Artificial Intelligence Review
Two layered Genetic Programming for mixed-attribute data classification
Applied Soft Computing
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Genetic programming for auction based scheduling
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Lazy learning for multi-class classification using genetic programming
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Hi-index | 0.00 |
Multi-objective optimization has played a major role in solving problems where two or more conflicting objectives need to be simultaneously optimized. This paper presents a Multi-Objective grammar-based genetic programming (MOGGP) system that automatically evolves complete rule induction algorithms, which in turn produce both accurate and compact rule models. The system was compared with a single objective GGP and three other rule induction algorithms. In total, 20 UCI data sets were used to generate and test generic rule induction algorithms, which can be now applied to any classification data set. Experiments showed that, in general, the proposed MOGGP finds rule induction algorithms with competitive predictive accuracies and more compact models than the algorithms it was compared with.