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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 |
URI: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4983011&tag=1 http://bura.brunel.ac.uk/handle/2438/5859 |
DOI: | http://dx.doi.org/10.1109/CEC.2009.4983011 |
ISBN: | 978-1-4244-2958-5 |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
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