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DC Field | Value | Language |
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dc.contributor.author | Yang, S | - |
dc.contributor.author | Richter, H | - |
dc.date.accessioned | 2011-09-26T10:45:56Z | - |
dc.date.available | 2011-09-26T10:45:56Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | 2009 IEEE Congress on Evolutionary Computation, Trondheim: 682 - 689, 18 - 21 May 2009 | en_US |
dc.identifier.isbn | 978-1-4244-2958-5 | - |
dc.identifier.uri | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4983011&tag=1 | en |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/5859 | - |
dc.description | This article is posted here here with permission from IEEE - Copyright @ 2009 IEEE | en_US |
dc.description.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. | en_US |
dc.description.sponsorship | The work by Shengxiang Yang was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Computer science | en_US |
dc.subject | Constraint optimization | en_US |
dc.subject | Convergence | en_US |
dc.subject | Councils | en_US |
dc.subject | Evolutionary computation | en_US |
dc.subject | Genetics | en_US |
dc.subject | Heuristic algorithms | en_US |
dc.subject | Performance analysis | en_US |
dc.subject | Statistics | en_US |
dc.subject | Testing | en_US |
dc.title | Hyper-learning for population-based incremental learning in dynamic environments | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/CEC.2009.4983011 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel (Active) | - |
pubs.organisational-data | /Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths | - |
pubs.organisational-data | /Brunel/Research Centres (RG) | - |
pubs.organisational-data | /Brunel/Research Centres (RG)/CIKM | - |
pubs.organisational-data | /Brunel/School of Information Systems, Computing and Mathematics (RG) | - |
pubs.organisational-data | /Brunel/School of Information Systems, Computing and Mathematics (RG)/CIKM | - |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
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