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Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-34859-4_34
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dc.titleAutomatic discovery of optimisation search heuristics for two dimensional strip packing using genetic programming
dc.contributor.authorNguyen, S.
dc.contributor.authorZhang, M.
dc.contributor.authorJohnston, M.
dc.contributor.authorTan, K.C.
dc.date.accessioned2014-06-19T03:01:06Z
dc.date.available2014-06-19T03:01:06Z
dc.date.issued2012
dc.identifier.citationNguyen, S.,Zhang, M.,Johnston, M.,Tan, K.C. (2012). Automatic discovery of optimisation search heuristics for two dimensional strip packing using genetic programming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7673 LNCS : 341-350. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-34859-4_34" target="_blank">https://doi.org/10.1007/978-3-642-34859-4_34</a>
dc.identifier.isbn9783642348587
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/69471
dc.description.abstractThis paper presents a genetic programming based hyper-heuristic (GPHH) for automatic discovery of optimisation heuristics for the two dimensional strip packing problem (2D-SPP). The novelty of this method is to integrate both the construction and improvement procedure into a heuristic which can be evolved by genetic programming (GP). The experimental results show that the evolved heuristics are very competitive and sometimes better than the popular state-of-the-art optimisation search heuristics for 2D-SPP. Moreover, the evolved heuristics can search for good packing solutions in a much more efficient way compared to the other search methods. © 2012 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-34859-4_34
dc.sourceScopus
dc.subjectbin packing
dc.subjectgenetic programming
dc.subjectheuristics
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/978-3-642-34859-4_34
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume7673 LNCS
dc.description.page341-350
dc.identifier.isiutNOT_IN_WOS
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