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Learning behavior in abstract memory schemes for dynamic optimization problems

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journal contribution
posted on 01.12.2009, 14:06 by Hendrik Richter, Shengxiang Yang
Integrating memory into evolutionary algorithms is one major approach to enhance their performance in dynamic environments. An abstract memory scheme has been recently developed for evolutionary algorithms in dynamic environments, where the abstraction of good solutions is stored in the memory instead of good solutions themselves to improve future problem solving. This paper further investigates this abstract memory with a focus on understanding the relationship between learning and memory, which is an important but poorly studied issue for evolutionary algorithms in dynamic environments. The experimental study shows that the abstract memory scheme enables learning processes and hence efficiently improves the performance of evolutionary algorithms in dynamic environments.

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Citation

Soft Computing, 2009, 13 (12), pp. 1163-1173.

Published in

Soft Computing

Publisher

Springer Verlag

issn

1432-7643

Available date

01/12/2009

Publisher version

http://link.springer.com/article/10.1007/s00500-009-0420-6

Notes

This is the author's final draft of the paper published as Soft Computing, 2009, 13 (12), pp. 1163-1173. The original publication is available at www.springerlink.com. Doi: 10.1007/s00500-009-0420-6

Language

en

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