Cited By
View all- Burke EHyde MKendall G(2012)Grammatical Evolution of Local Search HeuristicsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2011.216040116:3(406-417)Online publication date: 1-Jun-2012
Subheuristic search and scalability in a hyperheuristic
Pages 609 - 610
Abstract
Our previous work has introduced a {hyperheuristic} (HH) approach based on Genetic Programming (GP). There, GP employs user-given languages where domain-specific local heuristics are used as primitives for producing specialised metaheuristics (MH). Here, we show that the GP-HH works well with simple generic languages over subheuristic primitives, dealing with increases of problem size and reduction of resources. The system produces effective and efficient MHs that deliver best results known in a chosen test domain. We also demonstrate that user-given, modest domain information allows the HH to produce an improvement over a previous best result from the literature.
References
[1]
G. Jayalakshmi and S. Sathiamoorthy, et. al. An hybrid genetic algorithm. International Journal of Computational Engineering Science, 2001.
[2]
R.E. Keller and R. Poli. Linear genetic programming of parsimonious metaheuristics. In D. Srinivasan et al., eds., 2007 IEEE CEC}, 2007.
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- Subheuristic search and scalability in a hyperheuristic
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July 2008
1814 pages
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Published: 12 July 2008
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View all- Burke EHyde MKendall G(2012)Grammatical Evolution of Local Search HeuristicsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2011.216040116:3(406-417)Online publication date: 1-Jun-2012
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