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.
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