By Topic

An A* like algorithm to improve the performance of genetic algorithms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Rao, G.R. ; Dept. of Comput. Sci. & Eng., Nat. Inst. of Eng., Mysore, India ; Gowda, K.C.

Genetic algorithms (GAs) often suffer from difficulties of convergence due to lack of guidelines for the selection process. Normally the selection is based on the current fitness of the individuals, evaluated by a fitness function. However, the present fitness of an individual need not always indicate its ability to improve further. In this work, we propose an A* like evaluation method, which takes into account not only the present fitness of the individual, but also an estimate of its scope for further improvisation. This simple improvement to simple GA has produced results comparable to specialised GA methods in selection problems

Published in:

Information, Decision and Control, 1999. IDC 99. Proceedings. 1999

Date of Conference:

1999