Please use this identifier to cite or link to this item:
|Web of Science®
|A genetic algorithm calibration method based on convergence due to genetic drift
|Information Sciences, 2008; 178(14):2857-2869
|Elsevier Science Inc
|Matthew S. Gibbs, Graeme C. Dandy and Holger R. Maier
|The selection of Genetic Algorithm (GA) parameters is a difficult problem, and if not addressed adequately, solutions of good quality are unlikely to be found. A number of approaches have been developed to assist in the calibration of GAs, however there does not exist an accepted method to determine the parameter values. In this paper, a GA calibration methodology is proposed based on the convergence of the population due to genetic drift, to allow suitable GA parameter values to be determined without requiring a trial-and-error approach. The proposed GA calibration method is compared to another GA calibration method, as well as typical parameter values, and is found to regularly lead the GA to better solutions, on a wide range of test functions. The simplicity and general applicability of the proposed approach allows suitable GA parameter values to be estimated for a wide range of situations. © 2008 Elsevier Inc. All rights reserved.
|Copyright © 2008 Elsevier Inc. All rights reserved.
|Appears in Collections:
|Aurora harvest 6
Civil and Environmental Engineering publications
Environment Institute publications
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.