- author = "M. H. Marghny and I. E. El-Semman",
- title = "Extracting Logical Classification Rules With Gene Expression Programming: Microarray Case Study",
- booktitle = "CGST International Conference on Artificial Intelligence and Machine Learning (AIML-05)",
- year = "2005",
- editor = "H. Elmahdy",
- pages = "11--16",
- address = "Cairo, Egypt",
- month = "19-21 " # dec,
- keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Data mining, classification rules, microarray",
-
URL = "
https://www.researchgate.net/profile/Marghny-M/publication/246155015_Extracting_Logical_Classification_Rules_With_Gene_Expression_Programming_Microarray_Case_Study/links/00b7d5321626e108b4000000/Extracting-Logical-Classification-Rules-With-Gene-Expression-Programming-Microarray-Case-Study.pdf",
-
URL = "
http://www.icgst.com/paper.aspx?pid=P1120535113",
-
URL = "http://www.icgst.com/AIML05/papers/P1120535113.pdf",
- size = "6 pages",
- abstract = "The benefits of finding trends in large volumes of data has driven to the development of data mining technology for over a decade. This paper presents an evolutionary approach for data mining based on enhanced version of gene expression programming (GEP). We enhance the original GEP technique by using a logical operators instead of mathematical ones to represent the chromosome validity evaluation, which results in unconstrained search of the genome space while still ensuring validity of the program's output, it has been demonstrated that GEP greatly surpasses the traditional tree-based GP for its simplicity, high efficiency, solution compactness and comprehensibility.",
-
notes = "Title etc. updated Dec
2022
http://www.icgst.com/AIML05/conference/index.html aiml2005@icgst.com
Dept. of Computer Science, Faculty of Computers and Information, Assuit University, Egypt",
