International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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SA-selection-based Genetic Algorithm for the Design of Fuzzy Controller

  • Han Chang-Wook (School of Electrical Engineering and Computer Science, Yeungnam University) ;
  • Park Jung-Il (School of Electrical Engineering and Computer Science, Yeungnam University)
  • Published : 2005.06.01
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Abstract

This paper presents a new stochastic approach for solving combinatorial optimization problems by using a new selection method, i.e. SA-selection, in genetic algorithm (GA). This approach combines GA with simulated annealing (SA) to improve the performance of GA. GA and SA have complementary strengths and weaknesses. While GA explores the search space by means of population of search points, it suffers from poor convergence properties. SA, by contrast, has good convergence properties, but it cannot explore the search space by means of population. However, SA does employ a completely local selection strategy where the current candidate and the new modification are evaluated and compared. To verify the effectiveness of the proposed method, the optimization of a fuzzy controller for balancing an inverted pendulum on a cart is considered.

Keywords

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