This paper introduces the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm. Evaluation on a diverse set of floating-point benchmark problems show that heuristic space diversity has a significant impact on hyper-heuristic performance. The increasing heuristic space diversity strategies performed the best out of all strategies tested. Good performance was also demonstrated with respect to another popular multi-method algorithm and the best performing constituent algorithm.
Reference:
Grobler, J, Engelbrecht, A.P, Kendall, G and Yadavalli, V.S.S. 2014. Heuristic space diversity management in a meta-hyper-heuristic framework. In: 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 6-11 July 2014
Grobler, J., Engelbrecht, A., Kendall, G., & Yadavalli, V. (2014). Heuristic space diversity management in a meta-hyper-heuristic framework. IEEE Xplore. http://hdl.handle.net/10204/7938
Grobler, J, AP Engelbrecht, G Kendall, and VSS Yadavalli. "Heuristic space diversity management in a meta-hyper-heuristic framework." (2014): http://hdl.handle.net/10204/7938
Grobler J, Engelbrecht A, Kendall G, Yadavalli V, Heuristic space diversity management in a meta-hyper-heuristic framework; IEEE Xplore; 2014. http://hdl.handle.net/10204/7938 .
2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 6-11 July 2014. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website.
