Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems
| dc.cclicence | N/A | en |
| dc.contributor.author | Yang, Shengxiang | en |
| dc.date.accessioned | 2017-03-21T15:29:34Z | |
| dc.date.available | 2017-03-21T15:29:34Z | |
| dc.date.issued | 2005 | |
| dc.description.abstract | Several approaches have been developed into evolutionary algorithms to deal with dynamic optimization problems, of which memory and random immigrants are two major schemes. This paper investigates the application of a direct memory scheme for univariate marginal distribution algorithms (UMDAs), a class of evolutionary algorithms, for dynamic optimization problems. The interaction between memory and random immigrants for UMDAs in dynamic environments is also investigated. Experimental study shows that the memory scheme is efficient for UMDAs in dynamic environments and that the interactive effect between memory and random immigrants for UMDAs in dynamic environments depends on the dynamic environments. | en |
| dc.funder | N/A | en |
| dc.identifier.citation | Yang, S. (2005) Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, 3, pp. 2560-2567 | en |
| dc.identifier.uri | http://hdl.handle.net/2086/13802 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | N/A | en |
| dc.publisher | IEEE Press | en |
| dc.researchgroup | Centre for Computational Intelligence | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.title | Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems | en |
| dc.type | Conference | en |