Abstract
In this paper, a hybrid genetic algorithm for blind signal separation that extracts the individual unknown independent source signals out of given linear signal mixture is presented. The proposed method combines a genetic algorithm with local search and is called the hybrid genetic algorithm. The implemented separation method is based on evolutionary minimization of the separated signal cross-correlation. The convergence behaviour of the network is demonstrated by presenting experimental separating signal results. A computer simulation example is given to demonstrate the effectiveness of the proposed method. The hybrid genetic algorithm blind signal separation performance is better than the genetic algorithm at directly minimizing the Kullback-Leibler divergence. Eventually, it is hopeful that this optimization approach can be helpful for blind signal separation engineers as a simple, useful and reasonable alternative.
| Original language | English |
|---|---|
| Title of host publication | Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings |
| Publisher | Springer Verlag |
| Pages | 954-963 |
| Number of pages | 10 |
| ISBN (Print) | 3540464840, 9783540464846 |
| Publication status | Published - 2006 Jan 1 |
| Event | 13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China Duration: 2006 Oct 3 → 2006 Oct 6 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 4234 LNCS - III |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 13th International Conference on Neural Information Processing, ICONIP 2006 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 06-10-03 → 06-10-06 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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The hybrid genetic algorithm for blind signal separation. / Shyr, Wen-Jye.
Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Springer Verlag, 2006. p. 954-963 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4234 LNCS - III).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - The hybrid genetic algorithm for blind signal separation
AU - Shyr, Wen-Jye
PY - 2006/1/1
Y1 - 2006/1/1
N2 - In this paper, a hybrid genetic algorithm for blind signal separation that extracts the individual unknown independent source signals out of given linear signal mixture is presented. The proposed method combines a genetic algorithm with local search and is called the hybrid genetic algorithm. The implemented separation method is based on evolutionary minimization of the separated signal cross-correlation. The convergence behaviour of the network is demonstrated by presenting experimental separating signal results. A computer simulation example is given to demonstrate the effectiveness of the proposed method. The hybrid genetic algorithm blind signal separation performance is better than the genetic algorithm at directly minimizing the Kullback-Leibler divergence. Eventually, it is hopeful that this optimization approach can be helpful for blind signal separation engineers as a simple, useful and reasonable alternative.
AB - In this paper, a hybrid genetic algorithm for blind signal separation that extracts the individual unknown independent source signals out of given linear signal mixture is presented. The proposed method combines a genetic algorithm with local search and is called the hybrid genetic algorithm. The implemented separation method is based on evolutionary minimization of the separated signal cross-correlation. The convergence behaviour of the network is demonstrated by presenting experimental separating signal results. A computer simulation example is given to demonstrate the effectiveness of the proposed method. The hybrid genetic algorithm blind signal separation performance is better than the genetic algorithm at directly minimizing the Kullback-Leibler divergence. Eventually, it is hopeful that this optimization approach can be helpful for blind signal separation engineers as a simple, useful and reasonable alternative.
UR - http://www.scopus.com/inward/record.url?scp=33750724086&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33750724086&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33750724086
SN - 3540464840
SN - 9783540464846
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 954
EP - 963
BT - Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PB - Springer Verlag
ER -