Parameter Tuning Method for Genetic Algorithm using Taguchi Orthogonal Array for Non-linear Multimodal Optimization Problem
Bhagyashri Naruka1, Ashwini Kumar Yadav2, Shweta Sharma3, Janesh Singh Rathore4 

1Bhagyashri Naruka, Assistant Professor, Amity University, Jaipur, Rajasthan, India.
2Ashwini Kumar Yadav, Amity University, Jaipur, Rajasthan, India.
3Shweta Sharma, Amity University, Jaipur, Rajasthan, India.
4Janesh Singh Rathore, Amity University, Jaipur, Rajasthan, India.

Manuscript received on 08 March 2019 | Revised Manuscript received on 16 March 2019 | Manuscript published on 30 July 2019 | PP: 2979-2987 | Volume-8 Issue-2, July 2019 | Retrieval Number: B27119078219/19©BEIESP | DOI: 10.35940/ijrte.B2711.078219
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Abstract: 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.
Index Terms: Genetic Algorithm, Parameter Optimization, Taguchi Orthogonal Array, ANOVA, Half-Normal Probability Plot, Griewank Function.

Scope of the Article: Web Algorithms