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Mathematical Problems in Engineering
Volume 2016, Article ID 4527402, 10 pages
Research Article

Simulation Experiment Exploration of Genetic Algorithm’s Convergence over the Relationship Advantage Problem

Department of Industrial Engineering, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China

Received 12 April 2016; Revised 14 June 2016; Accepted 15 June 2016

Academic Editor: László T. Kóczy

Copyright © 2016 Yabo Luo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Concentrating on the convergence analysis of Genetic Algorithm (GA), this study originally distinguishes two types of advantage sources: value advantage and relationship advantage. Accordingly, the quantitative feature, complete quantization feature, and the partial quantization feature in the fitness evaluation are proposed. Seven simulation experiments show that these two types of advantages have different convergence properties. For value advantage problems, GA has a good convergence. However, for a relationship advantage problem, only from the practical point of view, it is possible to get a feasible and even satisfactory solution through large-scale searching, but, in theory, however, the searching process is not convergent. Therefore, GA is not reliable to solve relationship advantage problems, to which most engineering problems involving combinatorial optimization belong. This study systematically shows convergence properties of “relationship advantage” through simulation experiments, which will be a new area for the further study on GA.