Fuzzy system identification through hybrid genetic algorithms

被引:2
|
作者
Tcholakian, AB
Martins, A
Pacheco, RCS
Barcia, RM
机构
关键词
genetic algorithms; fuzzy system learning; Baldwin's effect; hybrid systems;
D O I
10.1109/NAFIPS.1997.624079
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we propose a new Hybrid Genetic Algorithm for fuzzy system learning. The algorithm is based on the Baldwin's Effect with the inclusion of biological principles of learning. Rather than considering mutation as a stochastic event, we take into account results of biological experiences that seem to indicate individual capability of choosing the best mutation. The proposed adaptive model consists of two levels: (a) an evolutionary or global level, which works on the generation of populations at genetic code level; and (b) a learning or local level, which works at the time life of the agents with the individuals reacting to environmental stimulus. The method has been applied in well-known learning problems, with strong supremacy over other hybrid genetic approaches, particularly in terms of expressiveness of the learned fuzzy system.
引用
收藏
页码:428 / 433
页数:6
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