Journal of Applied Mathematics and Decision Sciences
Volume 2006 (2006), Article ID 70240, 16 pages
doi:10.1155/JAMDS/2006/70240
Mapping the convergence of genetic algorithms
College of Business and Economics, California State University-Fullerton, Fullerton 92834, CA, USA
Received 29 August 2005; Revised 25 April 2006; Accepted 6 June 2006
Copyright © 2006 Zvi Drezner and George A. Marcoulides. 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.
Abstract
This paper examines the convergence of genetic algorithms using a
cluster-analytic-type procedure. The procedure is illustrated with
a hybrid genetic algorithm applied to the quadratic assignment
problem. Results provide valuable insight into how population
members are selected as the number of generations increases and how
genetic algorithms approach stagnation after many generations.