×
  • Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content

Loading…

Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

Like this presentation? Why not share!

Limits of scalability of multiobjective estimation of distribution algorithms

by on Feb 16, 2007

  • 2,525 views

The paper analyzes the scalability of multiobjective estimation of distribution algorithms (MOEDAs) on a class of boundedly-difficult additively-separable multiobjective optimization problems. The ...

The paper analyzes the scalability of multiobjective estimation of distribution algorithms (MOEDAs) on a class of boundedly-difficult additively-separable multiobjective optimization problems. The paper illustrates that even if the linkage is correctly identified, massive multimodality of the search problems can easily overwhelm the nicher and lead to exponential scale-up. Facetwise models are subsequently used to propose a growth rate of the number of differing substructures between the two objectives to avoid the niching method from being overwhelmed and lead to polynomial scalability of MOEDAs.

Statistics

Views

Total Views
2,525
Views on SlideShare
2,516
Embed Views
9

Actions

Likes
1
Downloads
0
Comments
0

3 Embeds 9

http://www.slideshare.net 4
http://www.illigal.uiuc.edu 3
http://www.kumarasastry.com 2

Accessibility

Categories

Upload Details

Uploaded via SlideShare as Adobe PDF

Usage Rights

CC Attribution-NonCommercial-NoDerivs LicenseCC Attribution-NonCommercial-NoDerivs LicenseCC Attribution-NonCommercial-NoDerivs License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
Post Comment
Edit your comment

Limits of scalability of multiobjective estimation of distribution algorithms Limits of scalability of multiobjective estimation of distribution algorithms Presentation Transcript