By Topic

Solving graph partitioning problem using 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

3 Author(s)
Shazely, S. ; Fac. of Eng., Cairo Univ., Giza, Egypt ; Baraka, H. ; Abdel-Wahab, A.

The graph partitioning problem (GPP) is one of the fundamental multimodal, combinatorial problems that has many applications in computer science. Many deterministic algorithms have been devised to obtain a good solution for the GPP. This paper presents new techniques for discovering more than one solution to this problem using genetic algorithms. The techniques used are based upon applying niching methods to obtain multiple good solutions instead of only one solution. The paper also presents in detail a comparison between the results of a traditional method, simple genetic algorithm (SGA), and two niching methods, fitness sharing and deterministic crowding when applied to the graph partitioning problem

Published in:

Circuits and Systems, 1998. Proceedings. 1998 Midwest Symposium on

Date of Conference:

9-12 Aug 1998