Paper Info
Title
Dynamic Heuristic Set Selection for Cross-Domain Selection Hyper-heuristics
Abstract
Selection hyper-heuristics have proven to be effective in solving various real-world problems. Hyper-heuristics differ from traditional heuristic approaches in that they explore a heuristic space rather than a solution space. These techniques select constructive or perturbative heuristics to construct a solution or improve an existing solution respectively. Previous work has shown that the set of problem-specific heuristics made available to the hyper-heuristic for selection has an impact on the performance of the hyper-heuristic. Hence, there have been initiatives to determine the appropriate set of heuristics that the hyper-heuristic can select from. However, there has not been much research done in this area. Furthermore, previous work has focused on determining a set of heuristics that is used throughout the lifespan of the hyper-heuristic with no change to this set during the application of the hyper-heuristic. This paper investigates dynamic heuristic set selection (DHSS) which applies dominance to select the set of heuristics at different points during the lifespan of a selection hyper-heuristic. The DHSS approach was evaluated on the benchmark set for the CHeSC cross-domain hyper-heuristic challenge. DHSS was found to improve the performance of the best performing hyper-heuristic for this challenge.
Year
DOI
Venue
2021
10.1007/978-3-030-90425-8_3
THEORY AND PRACTICE OF NATURAL COMPUTING (TPNC 2021)
Keywords
DocType
Volume
Dynamic heuristic set selection, Selection perturbative hyper-heuristics, Cross-domain hyper-heuristics
Conference
13082
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Ahmed Hassan100.68
Nelishia Pillay223733.72