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

Haofan Zhang*, Lei Fang, Sheng Uei Guan

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


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 languageEnglish
Title of host publicationBio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers
Number of pages8
Publication statusPublished - 2011
Event7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
Duration: 11 Aug 201114 Aug 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6840 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Intelligent Computing, ICIC 2011


  • domain decomposition
  • genetic algorithm
  • incremental attribute learning
  • local fitness
  • task decomposition

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