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Title: Hyper-learning for population-based incremental learning in dynamic environments
Authors: Yang, S
Richter, H
Keywords: Computer science;Constraint optimization;Convergence;Councils;Evolutionary computation;Genetics;Heuristic algorithms;Performance analysis;Statistics;Testing
Issue Date: 2009
Publisher: IEEE
Citation: 2009 IEEE Congress on Evolutionary Computation, Trondheim: 682 - 689, 18 - 21 May 2009
Abstract: The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. Recently, the PBIL algorithm has been applied for dynamic optimization problems. This paper investigates the effect of the learning rate, which is a key parameter of PBIL, on the performance of PBIL in dynamic environments. A hyper-learning scheme is proposed for PBIL, where the learning rate is temporarily raised whenever the environment changes. The hyper-learning scheme can be combined with other approaches, e.g., the restart and hypermutation schemes, for PBIL in dynamic environments. Based on a series of dynamic test problems, experiments are carried out to investigate the effect of different learning rates and the proposed hyper-learning scheme in combination with restart and hypermutation schemes on the performance of PBIL. The experimental results show that the learning rate has a significant impact on the performance of the PBIL algorithm in dynamic environments and that the effect of the proposed hyper-learning scheme depends on the environmental dynamics and other schemes combined in the PBIL algorithm.
Description: This article is posted here here with permission from IEEE - Copyright @ 2009 IEEE
ISBN: 978-1-4244-2958-5
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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