Evolutionary Algorithms in Stabilization of Inverted Pendulum
S.Suganthi Amudhan1, Dwivedi Vedvyas J2, Bhavin Sedani3

1S.Suganthi Amudhan*, Assistant Professor, Electronics and Communication Engineering, Babaria Institute of Technology, Varnama, Gujarat, India.
2Dr. Dwivedi Vedvyas J, Pro Vice Chancellor, C U Shah University, Wadhwan City. Surendranagar Gujarat, India.
3Dr. Bhavin Sedani, Professor, Electronics and Communication Engineering, L.D. College of Engineering, Ahmedabad, Gujarat, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1447-1453| Volume-8 Issue-6, March 2020. | Retrieval Number: F7336038620/2020©BEIESP | DOI: 10.35940/ijrte.F7336.038620

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Genetic Algorithm (GA), Extended Compact Genetic Algorithm (eCGA), Marginal Product Model (MPM), ,Fuzzy Logic Controller(FLC).
Scope of the Article: Analysis Of Algorithms And Computational Complexity.