Recursive and incremental learning GA featuring problem-dependent rule-set

Haofan Zhang; Lei Fang; Sheng Uei Guan

doi:10.1007/978-3-642-24553-4_30

Recursive and incremental learning GA featuring problem-dependent rule-set

Haofan Zhang^*
, Lei Fang
, Sheng Uei Guan

^*Corresponding author for this work

Xi'an Jiaotong-Liverpool University
University of St Andrews

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

Abstract

Traditional rule-based classifiers training with Genetic Algorithms have their major weaknesses in the classification accuracy and training time. To resolve these drawbacks, this paper reviews Recursive Learning of Genetic Algorithm with Task Decomposition and Varied Rule Set (RLGA) and proposes its variation that features Incremental Attribute Learning (RLGA-I). Experiments show that both the proposed solutions dramatically reduce the training duration with better generalization accuracy.

Original language	English
Title of host publication	Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers
Pages	215-222
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-24553-4_30
Publication status	Published - 2011
Event	7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China Duration: 11 Aug 2011 → 14 Aug 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6840 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Intelligent Computing, ICIC 2011
Country/Territory	China
City	Zhengzhou
Period	11/08/11 → 14/08/11

Keywords

domain decomposition
genetic algorithm
incremental attribute learning
local fitness
task decomposition

Access to Document

10.1007/978-3-642-24553-4_30

Cite this

Zhang, H., Fang, L., & Guan, S. U. (2011). Recursive and incremental learning GA featuring problem-dependent rule-set. In Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers (pp. 215-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6840 LNBI). https://doi.org/10.1007/978-3-642-24553-4_30

Zhang, Haofan ; Fang, Lei ; Guan, Sheng Uei. / Recursive and incremental learning GA featuring problem-dependent rule-set. Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers. 2011. pp. 215-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Recursive and incremental learning GA featuring problem-dependent rule-set",

abstract = "Traditional rule-based classifiers training with Genetic Algorithms have their major weaknesses in the classification accuracy and training time. To resolve these drawbacks, this paper reviews Recursive Learning of Genetic Algorithm with Task Decomposition and Varied Rule Set (RLGA) and proposes its variation that features Incremental Attribute Learning (RLGA-I). Experiments show that both the proposed solutions dramatically reduce the training duration with better generalization accuracy.",

keywords = "domain decomposition, genetic algorithm, incremental attribute learning, local fitness, task decomposition",

author = "Haofan Zhang and Lei Fang and Guan, \{Sheng Uei\}",

year = "2011",

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

pages = "215--222",

booktitle = "Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers",

note = "7th International Conference on Intelligent Computing, ICIC 2011 ; Conference date: 11-08-2011 Through 14-08-2011",

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Zhang, H, Fang, L & Guan, SU 2011, Recursive and incremental learning GA featuring problem-dependent rule-set. in Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6840 LNBI, pp. 215-222, 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou, China, 11/08/11. https://doi.org/10.1007/978-3-642-24553-4_30

Recursive and incremental learning GA featuring problem-dependent rule-set. / Zhang, Haofan; Fang, Lei; Guan, Sheng Uei.
Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers. 2011. p. 215-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6840 LNBI).

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

TY - GEN

T1 - Recursive and incremental learning GA featuring problem-dependent rule-set

AU - Zhang, Haofan

AU - Fang, Lei

AU - Guan, Sheng Uei

PY - 2011

Y1 - 2011

N2 - Traditional rule-based classifiers training with Genetic Algorithms have their major weaknesses in the classification accuracy and training time. To resolve these drawbacks, this paper reviews Recursive Learning of Genetic Algorithm with Task Decomposition and Varied Rule Set (RLGA) and proposes its variation that features Incremental Attribute Learning (RLGA-I). Experiments show that both the proposed solutions dramatically reduce the training duration with better generalization accuracy.

AB - Traditional rule-based classifiers training with Genetic Algorithms have their major weaknesses in the classification accuracy and training time. To resolve these drawbacks, this paper reviews Recursive Learning of Genetic Algorithm with Task Decomposition and Varied Rule Set (RLGA) and proposes its variation that features Incremental Attribute Learning (RLGA-I). Experiments show that both the proposed solutions dramatically reduce the training duration with better generalization accuracy.

KW - domain decomposition

KW - genetic algorithm

KW - incremental attribute learning

KW - local fitness

KW - task decomposition

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DO - 10.1007/978-3-642-24553-4_30

M3 - Conference Proceeding

AN - SCOPUS:84862935451

SN - 9783642245527

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

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BT - Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers

T2 - 7th International Conference on Intelligent Computing, ICIC 2011

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ER -

Zhang H, Fang L, Guan SU. Recursive and incremental learning GA featuring problem-dependent rule-set. In Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers. 2011. p. 215-222. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-24553-4_30

Recursive and incremental learning GA featuring problem-dependent rule-set

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Keywords

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