A genetic algorithm optimized new structured neural network for multistage decision-making problem

被引：0
|
作者：
Yang, L [1 ]
Dai, Y [1 ]
Zhang, B [1 ]
Gao, Y [1 ]
机构：
[1] Northeastern Univ, Shenyang 110004, Peoples R China
来源：
PDCAT 2005: SIXTH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES, PROCEEDINGS | 2005年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
For the widely use of multistage decision-making problem in our normal life such as in the new research area of dynamic selection of composite web services, this paper exerts all its effort on proposing a new approach to solve such problem. Motivated by neural networks' high parallel performance and Genetic Algorithm's powerful computation, a novel Genetic Algorithm optimized neural network is proposed in this paper for this task. In order to make this algorithm more adaptable for multistage decision-making problem, a new neural network structure for implementing he algorithm is proposed which is a modification to the one used by Thomopoulos or Rauch and Winarske.
引用
收藏
页码：925 / 929
页数：5
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