By Topic

Iterative parallel and distributed genetic algorithms with biased initial population

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)
Nakamura, M. ; Dept. of Inf. Eng., Ryukyus Univ., Okinawa, Japan ; Yamashiro, N. ; Gong, Y.

This work proposes an iterative parallel and distributed genetic algorithm with biased initial population to solve large-scale combinatorial optimization problems. The proposed scheme is a master-slave style in which a master node manages searched space of slave nodes and assigns seeds to generate initial population to slaves for their restarting of evolution process. Our approach allows us as wide as possible searching by all the slave nodes in the beginning periods of the searching and then focused searching by multiple slaves on a certain spaces that seems to include good quality solutions. Computer experiment shows the effectiveness of our proposed scheme.

Published in:

Evolutionary Computation, 2004. CEC2004. Congress on  (Volume:2 )

Date of Conference:

19-23 June 2004