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The Automatic Design of Hyper-heuristic Framework with Gene Expression Programming for Combinatorial Optimization problems
| Citations: | 2 - 0 self |
BibTeX
@MISC{Sabar_theautomatic,
author = {Nasser R. Sabar and Masri Ayob and Graham Kendall and Senior Member and Rong Qu and Senior Member},
title = {The Automatic Design of Hyper-heuristic Framework with Gene Expression Programming for Combinatorial Optimization problems},
year = {}
}
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Abstract
IEEE Abstract—Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems or even instances, have different landscape structures and complexity, the design of efficient high level heuristics can have a dramatic impact on hyper-heuristic performance. In this work, instead of using human knowledge to design the high level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance solving process, the high level heuristic of the hyperheuristic
Keyphrases
combinatorial optimization problem automatic design gene expression programming hyper-heuristic framework high level human knowledge heuristic selection mechanism heuristic selection multiple problem heuristic design returned solution gene expression acceptance criterion dramatic impact different problem different landscape structure efficient high level heuristic individual problem tailor-made methodology hyper-heuristic performance high level heuristic ieee abstract hyper-heuristic approach
