Fuzzy coding of genetic algorithms

被引：32

作者：

Sharma, SK ^{[1
]}

Irwin, GW ^{[1
]}

机构：

[1] Queens Univ Belfast, Sch Elect & Elect Engn, Intelligent Syst & Control Grp, Belfast, Antrim, North Ireland

来源：

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2003年 / 7卷 / 04期

关键词：

binary coding; floating-point coding; fuzzy coding; genetic algorithm; gray coding; neural networks; nonlinear identification;

D O I：

10.1109/TEVC.2003.812217

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：344 / 355

页数：12

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