Volume 2, Issue 6, November 2017, Page: 89-97
Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm
Abid Hussain, Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
Yousaf Shad Muhammad, Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
Muhammad Nauman Sajid, Department of Software Engineering, Foundation University, Islamabad, Pakistan
Received: Sep. 27, 2017;       Accepted: Oct. 19, 2017;       Published: Nov. 20, 2017
DOI: 10.11648/j.mcs.20170206.12      View  2318      Downloads  152
Genetic algorithm’s performance is based on three major factors, which are selection criteria, crossover and mutation operators. Each factor has its own significant role but the selection criteria to choose parents from the population is the key role to running the genetic algorithm. There is a number of selection schemes that have been introduced in literature and all have their own advantages. Most of the selection criterion is chose the parents which give highly optimum values based on the theory that healthy parents produce healthy offspring. In the current study, we proposed a new selection scheme which selects healthy parents as well as unhealthy parents. The novel selection scheme is simple to implement, and it has notable ability to reduce the effected of premature convergence compared to other selection schemes. We apply this new technique along with some traditional selection schemes on six benchmark problems and Simulation studies show a remarkable performance of the proposed selection scheme.
Genetic Algorithm, Genetic Operators, Selection Criterion, Benchmark Problems
To cite this article
Abid Hussain, Yousaf Shad Muhammad, Muhammad Nauman Sajid, Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm, Mathematics and Computer Science. Vol. 2, No. 6, 2017, pp. 89-97. doi: 10.11648/j.mcs.20170206.12
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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