Learning behavior in abstract memory schemes for dynamic optimization problems

Authors:
Hendrik Richter;Shengxiang Yang
Affiliations:
Institut Mess-, Steuerungs- und Regelungstechnik, HTWK Leipzig, Fachbereich Elektrotechnik und Informationstechnik, Postfach 30 11 66, 04125, Leipzig, Germany;University of Leicester, Department of Computer Science, University Road, LE1 7RH, Leicester, UK
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on ICNC-FSKD’2008;Guest Editors: Liang Zhao, Maozu Guo, Lipo Wang
Year:
2009

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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.