Publication Type:

Journal Article

Source:

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 6466 LNCS, Chennai, p.344-350 (2010)

ISBN:

3642175627; 9783642175626

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-78650892272&partnerID=40&md5=f60c248bee12b9f75400b34bd85fdb25

Keywords:

Analysis of means, Benchmark functions, Design, Design factors, Gap model, Genetic algorithms, Lakes, Mathematical models, Optimal combination, Parameter estimation, Parametric study, Performance efficacy, Population sizes, Population statistics, Simplex Crossover, Taguchi, Taguchi methods

Abstract:

In this paper, a parametric study of Generalized Generation Gap (G3) Genetic Algorithm (GA) model with Simplex crossover (SPX) using Taguchi method has been presented. Population size, number of parents and offspring pool size are considered as design factors with five levels. The analysis of mean factor is conducted to find the influence of design factors and their optimal combination for six benchmark functions. The experimental results suggest more experiments on granularity of design factor levels for better performance efficacy. © 2010 Springer-Verlag.

Notes:

cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@5a8ace23 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@4ee38a45 Through org.apache.xalan.xsltc.dom.DOMAdapter@3c8ae079; Conference Code:83340

Cite this Research Publication

S. Thangavelu and Velayutham, C. S., “Taguchi method based parametric study of generalized generation gap genetic algorithm model”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6466 LNCS, pp. 344-350, 2010.