TWO-STRATEGY REINFORCEMENT EVOLUTIONARY ALGORITHM USING DATA-MINING BASED CROSSOVER STRATEGY WITH TSK-TYPE FUZZY CONTROLLERS
Sheng‐Fuu Lin, Yi Chang Cheng · International journal of innovative computing, information & control · 2010
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 evolution. 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.