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

Department of Computing

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.",

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

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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

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AU - Fang, Lei

AU - Guan, Sheng Uei

PY - 2011

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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.

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KW - genetic algorithm

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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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