Two-strategy reinforcement evolutionary algorithm using data-mining based crossover strategy with TSK-type fuzzy controllers

Sheng-Fuu Lin*, Yi Chang Cheng

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

This paper proposes a two-strategy reinforcement evolutionary algorithm using data-mining crossover strategy (TSR-EADCS) with a TSK-type fuzzy controller (TFC) for solving various control problems. The purpose of the R-EADCS is not only to improve the design of traditional reinforcement signal but also to determine the suitable rules in a TFC and the suitable groups that are selected to perform crossover operation. Therefore, this paper proposes a two-strategy reinforcement signal to improve the performance of the traditional reinforcement signal design and uses the data mining technique to find suitable fuzzy rules and groups for evolutionn. The proposed TSR-EADCS consists of both structure and parameter learning. In structure learning, the TSR-EADCS uses the self adaptive method to determine the suitability of TFC models between different numbers of fuzzy rules. In parameter learning, the TSR-EADCS uses the data-mining crossover strategy which is based on frequent pattern growth to select the suitable groups that are used to perform crossover operation. Illustrative examples are conducted to show the performance and applicability of the TSR-EADCS.

Original languageEnglish
Pages (from-to)3863-3885
Number of pages23
JournalInternational Journal of Innovative Computing, Information and Control
Volume6
Issue number9
StatePublished - 1 Sep 2010

Keywords

  • Control
  • FP-growth
  • Fuzzy system
  • Reinforcement learning
  • Symbiotic evolution

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