Hyper-learning for population-based incremental learning in dynamic environments.
dc.contributor.author | Yang, Shengxiang | en |
dc.contributor.author | Richter, Hendrik | en |
dc.date.accessioned | 2013-06-11T15:46:34Z | |
dc.date.available | 2013-06-11T15:46:34Z | |
dc.date.issued | 2009 | |
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 |
dc.identifier.citation | Yang, L. and Richter, H. (2009) Hyper-learning for population-based incremental learning in dynamic environments . In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, 2009. New York: IEEE, pp. 682-689. | en |
dc.identifier.doi | https://doi.org/10.1109/CEC.2009.4983011 | |
dc.identifier.isbn | 978-1-4244-2958-5 | |
dc.identifier.uri | http://hdl.handle.net/2086/8720 | |
dc.language.iso | en | en |
dc.peerreviewed | Yes | en |
dc.publisher | IEEE | en |
dc.researchgroup | Centre for Computational Intelligence | en |
dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
dc.subject | Evolutionary computation | en |
dc.subject | Learning (artificial intelligence) | en |
dc.subject | Optimisation | en |
dc.subject | Dynamic optimization problems | en |
dc.subject | Hyper-learning scheme | en |
dc.subject | Hypermutation schemes | en |
dc.subject | Population-based incremental learning algorithm | en |
dc.title | Hyper-learning for population-based incremental learning in dynamic environments. | en |
dc.type | Article | en |
Files
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 3.18 KB
- Format:
- Item-specific license agreed upon to submission
- Description: