The hybrid genetic algorithm for blind signal separation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

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 languageEnglish
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Pages954-963
Number of pages10
ISBN (Print)3540464840, 9783540464846
Publication statusPublished - 2006 Jan 1
Event13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China
Duration: 2006 Oct 32006 Oct 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4234 LNCS - III
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Neural Information Processing, ICONIP 2006
CountryChina
CityHong Kong
Period06-10-0306-10-06

Fingerprint

Hybrid Genetic Algorithm
Genetic algorithms
Genetic Algorithm
Kullback-Leibler Divergence
Cross-correlation
Local Search
Engineers
Computer Simulation
Computer simulation
Unknown
Optimization
Alternatives
Demonstrate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shyr, W-J. (2006). The hybrid genetic algorithm for blind signal separation. In Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings (pp. 954-963). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4234 LNCS - III). Springer Verlag.
Shyr, Wen-Jye. / The hybrid genetic algorithm for blind signal separation. Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Springer Verlag, 2006. pp. 954-963 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Shyr, W-J 2006, The hybrid genetic algorithm for blind signal separation. in Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4234 LNCS - III, Springer Verlag, pp. 954-963, 13th International Conference on Neural Information Processing, ICONIP 2006, Hong Kong, China, 06-10-03.

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 proceedingConference contribution

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Shyr W-J. The hybrid genetic algorithm for blind signal separation. In 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)).