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Hybridization of Taguchi and Genetic Algorithm to minimize iteration for optimization of solution

Author:
Vaghela, P.A., Prajapati, J.M.
Source:
MethodsX 2019 v.6 pp. 230-238
ISSN:
2215-0161
Subject:
Taguchi method, algorithms, experimental design
Abstract:
This paper describes a novel hybrid approach of Taguchi and Genetic Algorithm to minimize number of iteration for optimization of a solution of the problem. A Genetic algorithm is used for global optimization. In GA initial population is selected randomly. Taguchi method gives a uniform combination of variables for the given search area. Hence, instead of selecting the initial populations by random search select the initial population by Taguchi design techniques. It will reduce the number of iteration to obtain a solution. This is explained with illustration.It can be used for selecting initial population in an organized manner rather than random selection. It can reduce the number of iterations. It can be applicable to all optimization problems where Genetic Algorithm is used.
Agid:
6294082