By Topic

A study of a non-linear optimization problem using a distributed genetic algorithm

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)
Neves, N. ; Illinois Univ., Champaign, IL, USA ; Nguyen, A.-T. ; Torres, E.L.

Genetic algorithms have been used successfully as a global optimization method when the search space is very large. To characterize and analyze the performance of genetic algorithms on a cluster of workstations, a parallel version of the GENESIS 5.0 was developed using PVM 3.3. This version, called VMGENESIS, was used to study a nonlinear least-squares problem. Performance results show that linear speedups can be achieved if the basic distributed genetic algorithm is combined with a simple dynamic load-balancing mechanism. Results also show that the quality of search changes significantly with the number of processors involved in the computation and with the frequency of communication

Published in:

Parallel Processing, 1996. Vol.3. Software., Proceedings of the 1996 International Conference on  (Volume:2 )

Date of Conference:

12-16 Aug 1996