Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Classifier systems and genetic algorithms
Machine learning: paradigms and methods
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 3rd International Conference on Genetic Algorithms
Intelligent Data Analysis
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary design of decision trees for medical application
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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An autonomous evolutionary algorithm for constructing decision trees is presented. The algorithm requires no or minimal human interaction and shows some interesting properties when used on different medical datasets. The algorithm uses a non-standard implicit fitness evaluation in the selection phase of a co-evolving environment. Together with self-adaptation of evolution parameters and with some other improvements it can monitor and adjust its own behavior. The algorithm's capability to self-adapt to a given problem is used as a measure to predict if some dataset is just difficult or impossible to analyze. The autonomous algorithm on average produces very general solutions or gives no solution if the dataset is prone to the overfitting problem.