Open AccessParameter Tuning Method for Genetic Algorithm using Taguchi Orthogonal Array for Non-linear Multimodal Optimization Problem
Author(s) -
Bhagyashri Naruka,
Ashwini Kumar Yadav,
Shweta Sharma,
Janesh Singh Rathore
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b2711.078219
Subject(s) - taguchi methods , orthogonal array , matlab , solver , genetic algorithm , design of experiments , fractional factorial design , heuristic , algorithm , computer science , mathematical optimization , factorial , optimization problem , factorial experiment , mathematics , statistics , machine learning , mathematical analysis , operating system
Genetic algorithm (GA) is the most widely used meta-heuristic optimization algorithm that can solve complex large scale optimization problems successfully. The only problem lies with the setting of GA control parameters and their levels for the optimal performance of the algorithm. The statistical method can be used for parameter tuning that allows us to collect data properly. Also, it analyzes the collected data accurately and presents the appropriate results. For statistical analysis, Taguchi’s robust factorial design which is highly fractional in nature with a special set of L32 orthogonal array (OA) is used. Taguchi’s robust design is a proficient method to find an optimum solution with a minimal number of designs of experiment (DOE). Analysis of variance (ANOVA) is conducted to check whether GA control parameters are statistically significant or not for non-linear multimodal optimization problems. Taguchi OA design and ANOVA analysis experimental study is conducted using Stat-Ease software and for the Genetic Algorithm control parameter setting GA solver of MATLAB is used