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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gen, M. Ida, K. Kim, J. |
| Copyright Year | 1997 |
| Description | Author affiliation: Dept. of Ind. & Syst. Eng., Ashikaga Inst. of Technol., Japan (Gen, M.) |
| Abstract | Goal programming is a powerful method which involves multiobjectives and is one of the excellent model in many real-world problems. The goal programming is to establish specific goals for each priority level, formulate objective functions for each objective, and then seek a solution that minimize the deviations of these objective functions from their respective goals. Often, in real-world problems the objectives are imprecise (or fuzzy). Recently, genetic algorithms are used to solve many real-world problems and have received a great deal of attention about their ability as optimization techniques for multiobjective optimization problems. This paper is attempt to apply these genetic algorithms to the goal programming problems which involve imprecise (or fuzzy) nonlinear information. Finally, we try to get some numerical experiments which have multiobjectives, and imprecise nonlinear information, using goal programming and genetic algorithm. |
| Starting Page | 413 |
| Ending Page | 418 |
| File Size | 601313 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780339495 |
| DOI | 10.1109/ICEC.1997.592345 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1997-04-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Genetic algorithms Functional programming Linear programming Electronic mail Fuzzy sets Stochastic processes Ear |
| Content Type | Text |
| Resource Type | Article |
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