An Overview of Genetic Algorithm Applied to Control Engineering Problem

Authors

  • Kumud Kant Awasthi Associate Professor, Department of Life Sciences, Vivekananda Global University, Jaipur, India Author

Keywords:

Adaptive, Control Engineering, Evolutionary Computation, Genetic Algorithms, Multiobjective

Abstract

The most well-known evolutionary search  methods are Genetic Algorithms (GAs). Despite the fact  that they are often used in control engineering issues, they  are not presently a conventional item in the regulator engineer's toolbox. This may be due in portion to the datum that there are few broad overviews of the use of GAs to  regulator production issues now available, as well as the  fact that they are often reported on at computer science  conferences rather than control engineering conferences.  This article tries to address this gap by providing an  overview of current GA applications in the area of control  engineering. The hereditary calculation (GA) is a model or  deliberation of organic development in light of Charles  Darwin's hypothesis of normal choice, made by John  Holland and his associates during the 1960s and 1970s.  Holland is broadly attributed similar to the first to utilize  hybrid and recombination, change, and choice to the  investigation of versatile and counterfeit frameworks. The  hereditary calculation as a critical thinking strategy is  deficient without these hereditary administrators. From that  point forward, an assortment of hereditary calculation  variations have been created and applied to an assortment  of advancement issues, going from chart shading to design  acknowledgment, from discrete frameworks (like the  mobile sales rep issue) to persistent frameworks (e.g., the  effective plan of airfoils in advanced plane design), and  from monetary business sectors to multi-objective  designing streamlining. 

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Published

2021-09-30

How to Cite

An Overview of Genetic Algorithm Applied to Control Engineering Problem . (2021). International Journal of Innovative Research in Computer Science & Technology, 9(5), 62–66. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/11320