Lamarckian clonal selection algorithm with application

Wuhong He; Haifeng Du; Licheng Jiao; Jing Li

doi:10.1007/11550822_50

Lamarckian clonal selection algorithm with application

Wuhong He
, Haifeng Du
, Licheng Jiao
, Jing Li

Xidian University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, Lamarckism and Immune Clonal Selection Theory are integrated to form a new algorithm, Lamarckian Clonal Selection Algorithm (LCSA). In the novel algorithm, the idea that Lamarckian evolution described how organism can evolve through learning, namely the point of "Gain and Convey" is applied, then this kind of learning mechanism is introduced into Standard Clonal Selection Algorithm (SCSA). In the experiments, ten benchmark functions are used to test the performance of LCSA, and the impact of parameters for LCSA is studied with great care. Compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.

Original language	English
Title of host publication	Artificial Neural Networks
Subtitle of host publication	Biological Inspirations - ICANN 2005 - 15th International Conference Proceedings
Publisher	Springer Verlag
Pages	317-322
Number of pages	6
ISBN (Print)	3540287523, 9783540287520
DOIs	https://doi.org/10.1007/11550822_50
State	Published - 2005
Externally published	Yes
Event	15th International Conference on Artificial Neural Networks: Biological Inspirations, ICANN 2005 - Warsaw, Poland Duration: 11 Sep 2005 → 15 Sep 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3696 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Artificial Neural Networks: Biological Inspirations, ICANN 2005
Country/Territory	Poland
City	Warsaw
Period	11/09/05 → 15/09/05

Access to Document

10.1007/11550822_50

Cite this

He, W., Du, H., Jiao, L., & Li, J. (2005). Lamarckian clonal selection algorithm with application. In Artificial Neural Networks: Biological Inspirations - ICANN 2005 - 15th International Conference Proceedings (pp. 317-322). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3696 LNCS). Springer Verlag. https://doi.org/10.1007/11550822_50

He, Wuhong ; Du, Haifeng ; Jiao, Licheng et al. / Lamarckian clonal selection algorithm with application. Artificial Neural Networks: Biological Inspirations - ICANN 2005 - 15th International Conference Proceedings. Springer Verlag, 2005. pp. 317-322 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{909e4bf731b949058fd0afd75d8ffaef,

title = "Lamarckian clonal selection algorithm with application",

abstract = "In this paper, Lamarckism and Immune Clonal Selection Theory are integrated to form a new algorithm, Lamarckian Clonal Selection Algorithm (LCSA). In the novel algorithm, the idea that Lamarckian evolution described how organism can evolve through learning, namely the point of {"}Gain and Convey{"} is applied, then this kind of learning mechanism is introduced into Standard Clonal Selection Algorithm (SCSA). In the experiments, ten benchmark functions are used to test the performance of LCSA, and the impact of parameters for LCSA is studied with great care. Compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.",

author = "Wuhong He and Haifeng Du and Licheng Jiao and Jing Li",

year = "2005",

doi = "10.1007/11550822\_50",

language = "英语",

isbn = "3540287523",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "317--322",

booktitle = "Artificial Neural Networks",

note = "15th International Conference on Artificial Neural Networks: Biological Inspirations, ICANN 2005 ; Conference date: 11-09-2005 Through 15-09-2005",

}

He, W, Du, H, Jiao, L & Li, J 2005, Lamarckian clonal selection algorithm with application. in Artificial Neural Networks: Biological Inspirations - ICANN 2005 - 15th International Conference Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3696 LNCS, Springer Verlag, pp. 317-322, 15th International Conference on Artificial Neural Networks: Biological Inspirations, ICANN 2005, Warsaw, Poland, 11/09/05. https://doi.org/10.1007/11550822_50

Lamarckian clonal selection algorithm with application. / He, Wuhong; Du, Haifeng; Jiao, Licheng et al.
Artificial Neural Networks: Biological Inspirations - ICANN 2005 - 15th International Conference Proceedings. Springer Verlag, 2005. p. 317-322 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3696 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Lamarckian clonal selection algorithm with application

AU - He, Wuhong

AU - Du, Haifeng

AU - Jiao, Licheng

AU - Li, Jing

PY - 2005

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N2 - In this paper, Lamarckism and Immune Clonal Selection Theory are integrated to form a new algorithm, Lamarckian Clonal Selection Algorithm (LCSA). In the novel algorithm, the idea that Lamarckian evolution described how organism can evolve through learning, namely the point of "Gain and Convey" is applied, then this kind of learning mechanism is introduced into Standard Clonal Selection Algorithm (SCSA). In the experiments, ten benchmark functions are used to test the performance of LCSA, and the impact of parameters for LCSA is studied with great care. Compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.

AB - In this paper, Lamarckism and Immune Clonal Selection Theory are integrated to form a new algorithm, Lamarckian Clonal Selection Algorithm (LCSA). In the novel algorithm, the idea that Lamarckian evolution described how organism can evolve through learning, namely the point of "Gain and Convey" is applied, then this kind of learning mechanism is introduced into Standard Clonal Selection Algorithm (SCSA). In the experiments, ten benchmark functions are used to test the performance of LCSA, and the impact of parameters for LCSA is studied with great care. Compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.

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DO - 10.1007/11550822_50

M3 - 会议稿件

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Artificial Neural Networks

PB - Springer Verlag

T2 - 15th International Conference on Artificial Neural Networks: Biological Inspirations, ICANN 2005

Y2 - 11 September 2005 through 15 September 2005

ER -

He W, Du H, Jiao L, Li J. Lamarckian clonal selection algorithm with application. In Artificial Neural Networks: Biological Inspirations - ICANN 2005 - 15th International Conference Proceedings. Springer Verlag. 2005. p. 317-322. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11550822_50

Lamarckian clonal selection algorithm with application

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