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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Analysis of the initialization stage of a Pittsburgh approach learning classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Hierarchical evolution of linear regressors
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Natural coding: a more efficient representation for evolutionary learning
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
IEEE Transactions on Evolutionary Computation
Mining data streams with concept drifts using genetic algorithm
Artificial Intelligence Review
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IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
Information Sciences: an International Journal
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This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must therefore be tried until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside of one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We tested our system on real data from the UCI repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice.