Please use this identifier to cite or link to this item:
Title: Learning behavior in abstract memory schemes for dynamic optimization problems
Authors: Richter, Hendrik
Yang, Shengxiang
First Published: Oct-2009
Publisher: Springer Verlag
Citation: Soft Computing, 2009, 13 (12), pp. 1163-1173.
Abstract: 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.
DOI Link: 10.1007/s00500-009-0420-6
ISSN: 1432-7643
Type: Article
Description: 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 Doi: 10.1007/s00500-009-0420-6
Appears in Collections:Published Articles, Dept. of Computer Science

Files in This Item:
File Description SizeFormat 
SOCO09_2.pdf260.74 kBAdobe PDFView/Open

Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.