¹ Department of Basic Course Teaching, Beijing Union University, Beijing, China
² Tourism Institute, Beijing Union University, Beijing, China School of Management Engineering, Shan Dong Jianzhu University, Jinan, China

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Abstract—Genetic algorithm (GA) is a powerful method to solve constrained optimization problems (COPs). In this paper, a new fitness function based hybrid genetic optimization algorithm (NFFHGA) for COPs is proposed, in which a new crossover operator based on Union Design is presented, and inspired by the smooth function technique, a new fitness function is designed to automatically search for potential solutions. Furthermore, in order to make the fitness function work well, a special technique which keeps a certain number of feasible solutions is also used. Experiments on 6 benchmark problems are performed and the compared results with the best known solutions reported in literature show that NFFHGA can not only quickly converge to the optimal or near-optimal solutions, but also have a high performance.

Index Terms—Constrained optimization, genetic algorithm, fitness function, Uniform Design

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Cite: Da-lian Liu, Xiao-hua Chen, and Jin-ling Du, " A Hybrid Genetic Algorithm for Constrained Optimization Problems," Journal of Computers vol. 8, no. 2, pp. 272-278, 2013.