Evolving Genetic Programming Classifiers with Loop Structures

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Abdulhamid:2012:CEC,

• title = "Evolving Genetic Programming Classifiers with Loop Structures",
• author = "Fahmi Abdulhamid and Andy Song and Kourosh Neshatian and Mengjie Zhang",
• pages = "2710--2717",
• booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation",
• year = "2012",
• editor = "Xiaodong Li",
• month = "10-15 " # jun,
• DOI = "doi:10.1109/CEC.2012.6252877",
• address = "Brisbane, Australia",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Conflict of Interest Papers, Classification, clustering, data analysis and data mining",
• abstract = "Loop structure is a fundamental flow control in programming languages for repeating certain operations. It is not widely used in Genetic Programming as it introduces extra complexity in the search. However in some circumstances, including a loop structure may enable GP to find better solutions. This study investigates the benefits of loop structures in evolving GP classifiers. Three different loop representations are proposed and compared with other GP methods and a set of traditional classification methods. The results suggest that the proposed loop structures can outperform other methods. Additionally the evolved classifiers can be small and simple to interpret. Further analysis on a few classifiers shows that they indeed have captured genuine characteristics from the data for performing classification.",
• notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.",

}

Genetic Programming entries for Fahmi Abdulhamid Andy Song Kourosh Neshatian Mengjie Zhang

Citations