We have located links that may give you full text access.
Hypervolume Subset Selection with Small Subsets.
Evolutionary Computation 2018 October 27
The hypervolume subset selection problem (HSSP) aims at approximating a set of n multidimensional points in [Formula: see text] with an optimal subset of a given size. The size k of the subset is a parameter of the problem, and an approximation is considered best when it maximizes the hypervolume indicator. This problem has proved popular in recent years as a procedure for multiobjective evolutionary algorithms. Efficient algorithms are known for planar points [Formula: see text], but there are hardly any results on HSSP in larger dimensions [Formula: see text]. So far, most algorithms in higher dimensions essentially enumerate all possible subsets to determine the optimal one, and most of the effort has been directed toward improving the efficiency of hypervolume computation. We propose efficient algorithms for the selection problem in dimension 3 when either k or [Formula: see text] is small, and extend our techniques to arbitrary dimensions for [Formula: see text].
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app