Please use this identifier to cite or link to this item:
Title: Learning behavior in abstract memory schemes for dynamic optimization problems
Authors: Richter, H
Yang, S
Keywords: Evolutionary algorithm;Dynamic optimization problem;Learning;Memory dynamics
Issue Date: 2009
Publisher: Springer Verlag
Citation: Soft Computing, 13(12): 1163 - 1173, Oct 2009
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.
Description: This is the post-print version of this article. The official article can be accessed from the link below - Copyright @ 2009 Springer Verlag
ISSN: 1432-7643
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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

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