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|Scopus||Web of Science®|
|Title:||An adaptive data structure for evolutionary multi-objective algorithms with unbounded archives|
|Citation:||Proceedings of the 2012 IEEE Congress on Evolutionary Computation, held in Brisbane, 10-15 June, 2012: pp.1-8|
|Series/Report no.:||IEEE Congress on Evolutionary Computation|
|Conference Name:||IEEE Congress on Evolutionary Computation (2012 : Brisbane, Qld.)|
|J. Yuen, S. Gao, M. Wagner and F. Neumann|
|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.|
|Rights:||© Copyright 2012 IEEE - All rights reserved.|
|Appears in Collections:||Computer Science publications|
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