Created by W.Langdon from gp-bibliography.bib Revision:1.4014

@InProceedings{smith:2006:IDEAL, author = "James F. {Smith, III} and ThanhVu H. Nguyen", title = "Guiding Genetic Program Based Data Mining Using Fuzzy Rules", booktitle = "Intelligent Data Engineering and Automated Learning IDEAL 2006", year = "2006", editor = "Emilio Corchado and HujunYin and Vicente Botti and Colin Fyfe", volume = "4224", series = "Lecture Notes in Computer Science", pages = "1337--1345", address = "Burgos, Spain", month = sep # " 20-23", publisher = "Springer", note = "Special Session on Nature-Inspired Date Technologies", keywords = "genetic algorithms, genetic programming, Fuzzy Logic, Data Mining, Control Algorithms, Planning Algorithms, UAV", isbn13 = "978-3-540-45485-4", DOI = "doi:10.1007/11875581_159", size = "9 pages", abstract = "A data mining procedure for automatic determination of fuzzy decision tree structure using a genetic program is discussed. A genetic program (GP) is an algorithm that evolves other algorithms or mathematical expressions. Methods for accelerating convergence of the data mining procedure are examined. The methods include introducing fuzzy rules into the GP and a new innovation based on computer algebra. Experimental results related to using computer algebra are given. Comparisons between trees created using a genetic program and those constructed solely by interviewing experts are made. Connections to past GP based data mining procedures for evolving fuzzy decision trees are established. Finally, experimental methods that have been used to validate the data mining algorithm are discussed.", notes = "A genetic program (GP) has been used as a data mining (DM) function to automatically create decision logic for two different resource managers (RMs). The most recent of the RMs, referred to as the UAVRM is the topic of this paper. It automatically controls a group of unmanned aerial vehicles (UAVs) that are cooperatively making atmospheric measurements. The DM procedure that uses a GP as a data mining function to create a subtree of UAVRM is discussed. The resulting decision logic for the RMs is rendered in the form of fuzzy decision trees. The fitness function, bloat control methods, data base, etc., for the tree to be evolved are described. Innovative bloat control methods using computer algebra based simplification are given. A subset of the fuzzy rules used by the GP to help accelerate convergence of the GP and improve the quality of the results is provided. Experimental methods of validating the evolved decision logic are discussed to support the effectiveness of the data mined results.", }

Genetic Programming entries for James F Smith III ThanhVu Nguyen