Lamarckian Clonal Selection Algorithm based function optimization

Wuhong He; Haifeng Du; Licheng Jiao; Jing Li

doi:10.1007/11494669_12

Lamarckian Clonal Selection Algorithm based function optimization

Wuhong He
, Haifeng Du
, Licheng Jiao
, Jing Li

Xidian University

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

Based on Lamarckism and Immune Clonal Selection Theory, Lamarckian Clonal Selection Algorithm (LCSA) is proposed in this paper. 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). Through the experimental results of optimizing complex multimodal functions, compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.

Original language	English
Pages (from-to)	91-98
Number of pages	8
Journal	Lecture Notes in Computer Science
Volume	3512
DOIs	https://doi.org/10.1007/11494669_12
State	Published - 2005
Externally published	Yes
Event	8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems - Vilanova i la Geltru, Spain Duration: 8 Jun 2005 → 10 Jun 2005

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10.1007/11494669_12

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title = "Lamarckian Clonal Selection Algorithm based function optimization",

abstract = "Based on Lamarckism and Immune Clonal Selection Theory, Lamarckian Clonal Selection Algorithm (LCSA) is proposed in this paper. 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). Through the experimental results of optimizing complex multimodal functions, 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 = "10.1007/11494669\_12",

language = "英语",

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journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

note = "8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems ; Conference date: 08-06-2005 Through 10-06-2005",

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T1 - Lamarckian Clonal Selection Algorithm based function optimization

AU - He, Wuhong

AU - Du, Haifeng

AU - Jiao, Licheng

AU - Li, Jing

PY - 2005

Y1 - 2005

N2 - Based on Lamarckism and Immune Clonal Selection Theory, Lamarckian Clonal Selection Algorithm (LCSA) is proposed in this paper. 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). Through the experimental results of optimizing complex multimodal functions, compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.

AB - Based on Lamarckism and Immune Clonal Selection Theory, Lamarckian Clonal Selection Algorithm (LCSA) is proposed in this paper. 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). Through the experimental results of optimizing complex multimodal functions, compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.

UR - https://www.scopus.com/pages/publications/25144475888

U2 - 10.1007/11494669_12

DO - 10.1007/11494669_12

M3 - 会议文章

AN - SCOPUS:25144475888

SN - 0302-9743

VL - 3512

SP - 91

EP - 98

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - 8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems

Y2 - 8 June 2005 through 10 June 2005

ER -

Lamarckian Clonal Selection Algorithm based function optimization

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