By Topic

Estimation of distribution algorithms: basic ideas and future directions

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

1 Author(s)
Ying-ping Chen ; Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan

Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms which can be regarded as abstraction of genetic algorithms (GAs) because in the design of EDAs, the population, one of the GA distinctive features, is replaced by probabilistic models/distributions. Building and sampling from the models substitute for the common genetic operators, such as crossover and mutation. Due to their excellent optimization performance, EDAs have been intensively studied and extensively applied in recent years. In order to interest more people to join the research of EDAs, this paper plays as an entry level introduction to EDAs. It starts with introducing the origination and basic ideas of EDAs, followed by presenting the current EDA frameworks, which are broadly applied in many scientific and engineering disciplines. Finally, this paper also describes some ongoing topics and potential directions in the hope that readers may get further insights into EDAs.

Published in:

World Automation Congress (WAC), 2010

Date of Conference:

19-23 Sept. 2010