.png){kind=icon}
.png){kind=icon}
Return
Fuzzy nonlinear goal programming using genetic algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/S0360-8352(97)00036-3.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Goal programming(GP) is a powerful method which involves multiobjectives and is one of the excellent models in many real-world problems. The goal programming is to establish specific goals for each priorty level, formulate objective functions for each goal, 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. (C) 1997 Elsevier Science Ltd.
Keywords:
goal programming (GP)
fuzzy nonlinear programming
genetic algorithm (GA)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
1.0W
Citations:
3.8W
Organization
No organization information available