C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An efficient data structure for decision rules discovery
Proceedings of the 2003 ACM symposium on Applied computing
Natural coding: a more efficient representation for evolutionary learning
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Knowledge-based fast evaluation for evolutionary learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary learning of hierarchical decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Evolutionary algorithms appear as an interesting alternative to achieve minimal error rates and low numbers of rules in supervised learning tasks. In spite of the computational cost of this approach, some proposals can be applied to make the algorithm faster and more efficient. This paper describes some of these proposals, which are integrated in the evolutionary tool HIDER*. Specifically, we developed a new genetic encoding for the individuals of the evolutionary population and a novel data structure for the evaluation process. These approaches allow the evolutionary algorithms to reduce the high computational cost and to obtain high quality solutions.