Fuzzy Genetic Algorithms Based on Level Interval Algorithm
Zhang, Jinglan, Pham, Binh L., & Chen, Yi-Ping Phoebe (2001) Fuzzy Genetic Algorithms Based on Level Interval Algorithm. In Kazmierczak, E. (Ed.) 10th IEEE International Conference on Fuzzy Systems, 2-5 December, Melbourne, Australia.
Many decisions need to be made based on imprecise or incomplete initial information. In such cases, decision makers are generally more interested in sets of the most promising solutions rather than the best single solution. Therefore, in contrast to conventional optimisation approaches that aim to find exact optimal points, we aim to find optimal ranges with variable satisfaction degrees. This paper presents a fuzzy-set-based approach for the representation and optimisation of practical problems with imprecise property where evolutionary computation is used for obtaining fuzzy solutions through guided searching. The representation of fuzzy sets, its initialisation, crossover, mutation, and validation, the ranking approach for fuzzy objective values, and the propagation method of fuzzy information are discussed. Several examples for illustrating the fuzzy evolutionary optimisation approach are provided.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||fuzzy optimisation, fuzzy genetic algorithms, propagation of imprecise information|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2001 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||18 Oct 2005|
|Last Modified:||03 Mar 2011 15:41|
Repository Staff Only: item control page