Multi-Population Genetic Algorithms for Tuning Fuzzy Logic Controller

2007 ◽  
Vol 2 (2) ◽  
pp. 56-63
Author(s):  
Munther N. Al-Tikriti ◽  
◽  
Rokaia Sh. Al-Joubori ◽  
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Population Genetic ◽  
Fuzzy Logic Controller

A self-learning fuzzy logic controller using genetic algorithms with reinforcements

1997 ◽  
Vol 5 (3) ◽  
pp. 460-467 ◽  
Author(s):  
Chih-Knan Chiang ◽  
Hung-Yuan Chung ◽  
Jin-Jye Lin
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Self Learning

scholarly journals Evolutionary Algorithms in Stabilization of Inverted Pendulum

2020 ◽  
Vol 8 (6) ◽  
pp. 1447-1453
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Test Bench ◽  
Design Parameters ◽  
Characteristic Parameters ◽  
Linkage Learning

A novel optimization algorithm of fuzzy logic controller (FLC) using genetic algorithms is used to characterize the major design parameters of an FLC known as characteristic parameters. The characteristic parameters simplify the design of FLC which are encoded into a chromosome as an integer string. These are optimized by maximizing the evaluated fitness through genetic operations to achieve the optimized FLC .An effective Genetic Algorithm (GA) is proposed using linkage learning, or building block identification. The genes arranged to have a fitness enhancement is the essence of linkage learning. A perfect and faster extended GA is suggested using an effective method to learn distributions and then by linking them. Stabilization of Inverted pendulum pole angle is taken as test bench.

Genetic algorithms with variable mutation rates: Application to fuzzy logic controller design

1997 ◽  
Vol 211 (2) ◽  
pp. 157-167 ◽  
Author(s):  
D T Pham ◽  
D Karaboga
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Mutation Rate ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Mutation Rates ◽  
Fuzzy Logic Controllers ◽  
Delayed System ◽  
Simulation Results ◽  
Logic Controllers

Three variable mutation rate strategies for improving the performance of genetic algorithms (GAs) are described. The problem of optimizing fuzzy logic controllers is used to evaluate a GA adopting these strategies against a GA employing a static mutation regime. Simulation results for a second-order time-delayed system controlled by fuzzy logic controllers (FLCs) obtained using the different GAs are presented.

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