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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yuen, J. Gao, S. Wagner, M. Neumann, F. |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Computer Science, The University of Adelaide, Australia (Yuen, J.; Gao, S.; Wagner, M.; Neumann, F.) |
| Abstract | Archives have been widely used in evolutionary multi-objective optimization in order to store the optimal points found so far during the optimization process. Usually the size of an archive is bounded which means that the number of points it can store is limited. This implies that knowledge about the set of non-dominated solutions that has been obtained during the optimization process gets lost. Working with unbounded archives allows to keep this knowledge which can be useful for the progress of an evolutionary multi-objective algorithm. In this paper, we propose an adaptive data structure for dealing with unbounded archives. This data structure allows to traverse the archive efficiently and can also be used for sampling solutions from the archive which can be used for reproduction. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 868105 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467315104 |
| e-ISBN | 9781467315098 |
| e-ISBN | 9781467315081 |
| DOI | 10.1109/CEC.2012.6256468 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Optimization Runtime Data structures Partitioning algorithms Complexity theory Vectors Evolutionary computation Multi-Objective Optimization Archive Data Structures Evolutionary Algorithm |
| Content Type | Text |
| Resource Type | Article |
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