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Nonlinear mapping using real-valued genetic algorithm

โœ Scribed by Zeng-Ping Chen; Jian-Hui Jiang; Yang Li; Ru-Qin Yu

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
243 KB
Volume
45
Category
Article
ISSN
0169-7439

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โœฆ Synopsis

ลฝ

. A real-valued genetic algorithm RGA for the optimization problem of real-valued parameters was developed. Two special operators, orientated mutation and immigration, were introduced to enhance the efficiency of the searching process and confront the causes of premature convergence. The proposed RGA was applied to complicated nonlinear mapping problems. Experimental results show that the proposed RGA can alleviate the problem of premature convergence and successfully find acceptable optima with high efficiency.

๐Ÿ“œ SIMILAR VOLUMES

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โœ Yo-Ping Huang; Chih-Hsin Huang ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 807 KB

A genetic-based fuzzy grey prediction model is proposed in this paper. Instead of working on the conventional bit by bit operation, both the crossover and mutation operators are real-valued handled by the presented algorithms. To prevent the system from turning into a premature problem, we select th