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.
| Original language | English |
|---|---|
| Title of host publication | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| ISBN (Print) | 9781467315098 |
| DOIs | |
| Publication status | Published - 2012 |
| Externally published | Yes |
| Event | IEEE Congress on Evolutionary Computation 2012 - Brisbane, Australia Duration: 10 Jun 2012 → 15 Jun 2012 https://ieeexplore.ieee.org/xpl/conhome/6241678/proceeding (Proceedings) |
Conference
| Conference | IEEE Congress on Evolutionary Computation 2012 |
|---|---|
| Abbreviated title | IEEE CEC 2012 |
| Country/Territory | Australia |
| City | Brisbane |
| Period | 10/06/12 → 15/06/12 |
| Internet address |
Keywords
- Archive
- Data Structures
- Evolutionary Algorithm
- Multi-Objective Optimization