Genetic neural networks on MIMD computers
Genetic neural networks on MIMD computers
C4.5: programs for machine learning
C4.5: programs for machine learning
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on 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
A New Interpretation of Schema Notation that Overtums the Binary Encoding Constraint
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
General cardinality genetic algorithms
Evolutionary Computation
The simple genetic algorithm and the walsh transform: Part i, theory
Evolutionary Computation
The simple genetic algorithm and the walsh transform: Part ii, the inverse
Evolutionary Computation
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Evolutionary learning of hierarchical decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Knowledge discovery from doctor-patient relationship
Proceedings of the 2004 ACM symposium on Applied computing
Feature influence for evolutionary learning
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Wrapper discretization by means of estimation of distribution algorithms
Intelligent Data Analysis
An approach to reduce the cost of evaluation in evolutionary learning
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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To select an adequate coding is one of the main problems in applications based on Evolutionary Algorithms. Many codings have been proposed to represent the search space for obtaining decision rules. A suitable representation of the individuals of the genetic population can reduce the search space, so that the learning process is accelerated by decreasing the number of necessary generations to complete the task. In this sense, natural coding achieves such reduction and improves the results obtained by other codings. This paper justifies the use of natural coding by comparing it with hybrid coding that joins well-known binary and real representations. We have tested both codings on a heterogeneous subset of databases from the UCI Machine Learning Repository. The experiments' results show that natural coding improves the quality of the obtained knowledge-model using only one third of the generations that hybrid coding needs as well as a smaller population.