A New Evolutionary Method with Fuzzy Logic for Combining Particle Swarm Optimization and Genetic Algorithms: The Case of Neural Networks Optimization

被引：2
|
作者：
Valdez, Fevrier [1 ]
Melin, Patricia [2 ]
Mendoza, Olivia [1 ]
机构：
[1] Univ Autonoma Baja California, 12498 Roll Dr 1272, San Diego, CA 92154 USA
[2] Tijuana Inst Technol, Div Grad Studies & Res, Tijuana, Mexico
来源：
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 | 2008年
关键词：
D O I：
10.1109/IJCNN.2008.4634000
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The proposed hybrid method is also tested with the problem of neural network optimization. The new hybrid PSO+GA method is shown to be superior with respect to both the individual evolutionary methods.
引用
收藏
页码：1536 / 1543
页数：8
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