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dc.contributor.author Grobler, J
dc.contributor.author Engelbrecht, AP
dc.contributor.author Kendall, G
dc.contributor.author Yadavalli, VSS
dc.date.accessioned 2015-02-09T07:26:10Z
dc.date.available 2015-02-09T07:26:10Z
dc.date.issued 2014-12
dc.identifier.citation Grobler, J, Engelbrecht, A.P, Kendall, G and Yadavalli, V.S.S. 2014. The entity-to-algorithm allocation problem: Extending the analysis. In: IEEE Symposium Series on Computational Intelligence, Orlando USA, 9-12 December 2014 en_US
dc.identifier.uri http://www.graham-kendall.com/papers/geky2014a.pdf
dc.identifier.uri http://hdl.handle.net/10204/7856
dc.description IEEE Symposium Series on Computational Intelligence, Orlando USA, 9-12 December 2014 en_US
dc.description.abstract This paper extends the investigation into the algorithm selection problem in hyper-heuristics, otherwise referred to as the entity-to-algorithm allocation problem, introduced by Grobler et al.. Two newly developed population-based portfolio algorithms (the evolutionary algorithm based on self-adaptive learning population search techniques (EEA-SLPS) and the Multi-EA algorithm) are compared to two metahyper-heuristic algorithms. The algorithms are evaluated under similar conditions and the same set of constituent algorithms on a diverse set of floating-point benchmark problems. One of the meta-hyper-heuristics are shown to outperform the other algorithms, with EEA-SLPS coming in a close second. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Workflow;14000
dc.subject Memetic algorithms en_US
dc.subject Hyper-heuristics en_US
dc.subject Metahyper-heuristic algorithms en_US
dc.title The entity-to-algorithm allocation problem: Extending the analysis en_US
dc.type Presentation en_US


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