A generic applied evolutionary hybrid technique

Author

Beligiannis, Grigorios ; Skarlas, Lambros ; Likothanassis, Spiridon

Volume

21

Issue

3

fYear

2004

fDate

5/1/2004 12:00:00 AM

Firstpage

28

Lastpage

38

Abstract

In this contribution, a generic applied evolutionary hybrid technique that combines the effectiveness of adaptive multimodel partitioning filters and genetic algorithm (GAs) robustness has been designed, developed, and applied in real-world adaptive system modeling and information mining problems. The method can be applied to linear and nonlinear real-world data, is not restricted to the Gaussian case, is computationally efficient, and is applicable to online/adaptive operation. Furthermore, it can be realized in a parallel processing fashion, a fact that makes it amenable to very large scale integration (VLSI) implementation.

Keywords

adaptive systems; autoregressive moving average processes; data mining; filtering theory; genetic algorithms; identification; parallel processing; adaptive multimodel partitioning filter; adaptive system modeling; autoregressive moving average model; generic applied evolutionary hybrid technique; genetic algorithm robustness; information mining problem; nonlinear real-world data; nonlinear system identification; very large scale integration implementation; Adaptive filters; Adaptive systems; Algorithm design and analysis; Genetic algorithms; Information filtering; Information filters; Modeling; Parallel processing; Robustness; Very large scale integration;

fLanguage

English

Journal_Title

Signal Processing Magazine, IEEE

Publisher

ieee

ISSN

1053-5888

Type

jour

DOI

10.1109/MSP.2004.1296540

Filename

1296540