A Memetic Genetic Programming with Decision Tree-based Local Search for Classification Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Wang:2011:AMGPwDTLSfCP,

• title = "A Memetic Genetic Programming with Decision Tree-based Local Search for Classification Problems",
• author = "Pu Wang and Ke Tang and Edward Tsang and Xin Yao",
• pages = "916--923",
• booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation",
• year = "2011",
• editor = "Alice E. Smith",
• month = "5-8 " # jun,
• address = "New Orleans, USA",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, area under ROC curve, classification problems, classifier, decision tree-based local search, fitness function, memetic computing, memetic genetic programming, statistical genetic decision tree, training algorithms, decision trees, learning (artificial intelligence), pattern classification, search problems",
• DOI = "doi:10.1109/CEC.2011.5949716",
• abstract = "In this work, we propose a new genetic programming algorithm with local search strategies, named Memetic Genetic Programming (MGP), for classification problems. MGP aims to acquire a classifier with large Area Under the ROC Curve (AUC), which has been proved to be a better performance metric for traditionally used metrics (e.g., classification accuracy). Three new ideas are presented in our new algorithm. First, a new representation called statistical genetic decision tree (SGDT) for GP is proposed on the basis of Genetic Decision Tree (GDT). Second, a new fitness function is designed by using statistic information from SGDT. Third, the concept of memetic computing is introduced into SGDT. As a result, the MGP is equipped with a local search method based on the training algorithms for decision trees. The efficacy of the MGP is empirically justified against a number of relevant approaches.",
• notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.",

}

Genetic Programming entries for Pu Wang Ke Tang Edward P K Tsang Xin Yao

Citations