By Topic

An improvement of database with local search mechanisms for genetic algorithms in large-scale computing environments

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
$33 $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)
Y. Hanada ; Graduate Sch. of Eng., Doshisha Univ., Kyoto, Japan ; T. Hiroyasu ; M. Miki

Recently, GA that uses large-scale computer systems comprised of massive processors has become feasible because of the emergence of super PC clusters and grid computation environments. Mechanisms to use massive computation resources laconically and to search effectively are necessary if large-scale computer systems are available. In this study, a new GA-specific database with the local search mechanism to assure the scalability of search performances against the number of computing resources is proposed. Our database possesses information of searched space, and in addition, the local search for nonsearched spaces is applied using individuals stored in the database. To embed our database in a GA enables us to comprehend the quantitative rate of a searched region during searches. Applying this local search, the searched space can be expanded linearly in accordance with the increase in computing resources and the exhaustive search is guaranteed under infinite computations. The features of the introduced GA are discussed with reference to several types of experiments. This method was applied to primitive functions and test functions of continuous optimization problems. Through such experiments, it was shown that our method ensures an effective exhaustive search and has almost the same performance as a conventional GA.

Published in:

2005 IEEE Congress on Evolutionary Computation  (Volume:3 )

Date of Conference:

2-5 Sept. 2005