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Fuzzy coding of genetic algorithms
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PRE
AI
DOI：10.1109/TEVC.2003.812217.png)
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摘要
En 中文
A new chromosome encoding method, named fuzzy coding, is proposed for representing real number parameters in a genetic algorithm. Fuzzy coding provides the value of a parameter on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree of membership. Thus, it represents the knowledge associated with each parameter and is an indirect method of encoding compared with alternatives, where the parameters are directly represented in the encoding. Fuzzy coding is described and compared with conventional binary coding, gray coding, and floating-point coding. Two test examples, along with neural identification of a nonlinear pH process from experimental data, are studied. It is shown that fuzzy coding is better than the conventional methods and is effective for parameter optimization in problems where the search space is complicated.
Keyword:
binary coding
floating-point coding
fuzzy coding
genetic algorithm
gray coding
neural networks
nonlinear identification
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
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