This paper expands on 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 in search of greater performance benefits. Evaluation of various strategies on a diverse set of floating-point benchmark problems shows that heuristic space diversity has a significant impact on hyper-heuristic performance. An exponentially increasing strategy (EIHH) obtained the best results. The value of a priori information about constituent algorithm performance on the benchmark set in question was also evaluated. Finally, EIHH demonstrated good performance when compared to a popular population based algorithm portfolio algorithm and the best performing constituent algorithm.
Reference:
Grobler, J, Engelbrecht, AP, Kendall, G and Yadavallie, VSS. 2015. Heuristic space diversity control for improved meta-hyper-heuristic performance. Information Sciences. Vol 300. pp 49-62
Grobler, J., Engelbrecht, A., Kendall, G., & Yadavallie, V. (2015). Heuristic space diversity control for improved meta-hyper-heuristic performance. http://hdl.handle.net/10204/8576
Grobler, J, AP Engelbrecht, G Kendall, and VSS Yadavallie "Heuristic space diversity control for improved meta-hyper-heuristic performance." (2015) http://hdl.handle.net/10204/8576
Grobler J, Engelbrecht A, Kendall G, Yadavallie V. Heuristic space diversity control for improved meta-hyper-heuristic performance. 2015; http://hdl.handle.net/10204/8576.
Copyright: 2015 Elsevier. 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. The definitive version of the work is published in Information Sciences, Vol 300, pp 49-62.
