JACIII Vol.11 No.4 pp. 358-364
doi: 10.20965/jaciii.2007.p0358


Real-Coded Genetic Algorithm for Solving Generalized Polynomial Programming Problems

Jui-Yu Wu and Yun-Kung Chung

Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 320, Taiwan

April 26, 2006
August 4, 2006
April 20, 2007
generalized polynomial programming, nonlinear programming, genetic algorithm

Generalized polynomial programming (GPP) is a nonlinear programming (NLP) method based on a nonconvex objective function, which is subject to nonconvex inequality constraints. Hence, a GPP problem has multiple local optima in its constrained solution space. General NLP techniques use local optimization, and therefore do not easily solve GPP problems. Some deterministic global optimization approaches have been developed to overcome this drawback of NLP methods. Although these approaches yield a global solution to a GPP problem, they can be mathematically tedious. Therefore, this study presents a real-coded genetic algorithm (RGA), which is a stochastic global optimization method, to find a global solution to a GPP problem. The proposed RGA is used to solve a set of GPP problems. The best solution obtained by the RGA is compared with the known global solution to each test problem. Numerical results show that the proposed RGA converges to a global solution to a GPP problem.

Cite this article as:
Jui-Yu Wu and Yun-Kung Chung, “Real-Coded Genetic Algorithm for Solving Generalized Polynomial Programming Problems,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.4, pp. 358-364, 2007.
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