Grobler, JEngelbrecht, APKendall, GYadavalli, VSS2015-02-092015-02-092014-12Grobler, 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 2014http://www.graham-kendall.com/papers/geky2014a.pdfhttp://hdl.handle.net/10204/7856IEEE Symposium Series on Computational Intelligence, Orlando USA, 9-12 December 2014This 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.enMemetic algorithmsHyper-heuristicsMetahyper-heuristic algorithmsConference PresentationGrobler, J., Engelbrecht, A., Kendall, G., & Yadavalli, V. (2014). The entity-to-algorithm allocation problem: Extending the analysis. IEEE. http://hdl.handle.net/10204/7856Grobler, J, AP Engelbrecht, G Kendall, and VSS Yadavalli. "The entity-to-algorithm allocation problem: Extending the analysis." (2014): http://hdl.handle.net/10204/7856Grobler J, Engelbrecht A, Kendall G, Yadavalli V, The entity-to-algorithm allocation problem: Extending the analysis; IEEE; 2014. http://hdl.handle.net/10204/7856 .TY - Conference Presentation AU - Grobler, J AU - Engelbrecht, AP AU - Kendall, G AU - Yadavalli, VSS AB - 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. DA - 2014-12 DB - ResearchSpace DP - CSIR KW - Memetic algorithms KW - Hyper-heuristics KW - Metahyper-heuristic algorithms LK - https://researchspace.csir.co.za PY - 2014 T1 - The entity-to-algorithm allocation problem: Extending the analysis TI - The entity-to-algorithm allocation problem: Extending the analysis UR - http://hdl.handle.net/10204/7856 ER -