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

Zvi Drezner and George A. Marcoulides

 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

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.