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This paper investigates a new method for improving generalization performance of Genetic Program ming (GP) on Binary Classifcation tasks. The scheme of self adaptive, individualized genetic operators combined with adap tive tournament size is designed to provide balanced exploration and exploitation. We test this scheme on several benchmark Binary Classifcation problems and fnd that the pro posed techniques deliver superior performance when compared with both a tuned GP confguration and a feedback adaptive

Author

Tom Sawyer

Language

EN