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Please use this identifier to cite or link to this item: http://hdl.handle.net/11667/85
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dc.contributorVeerapen, Nadarajen-
dc.contributor.otherEPSRC - Engineering and Physical Sciences Research Councilen_GB
dc.contributor.otherLeverhulme Trusten_GB
dc.creatorVeerapen, Nadarajen-
dc.creatorDaolio, Fabio-
dc.creatorOchoa, Gabriela-
dc.creatorTomassini, Marco-
dc.date.accessioned2017-01-20T08:59:03Z-
dc.date.available2017-01-20T08:59:03Z-
dc.date.created2016-10-
dc.identifier.urihttp://hdl.handle.net/11667/85-
dc.description.abstractThe dataset contains landscape data for "Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study", G. Ochoa, N. Veerapen, F. Daolio, M. Tomassini. The 17th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2017), 19-21 April 2017, Amsterdam, The Netherlands. The dataset describes the network structure of the local optima networks (LON) for Number Partitioning Problem (NPP) instances of size 10, 15 and 20, and with values of parameter k from 0.4 to 1.2 in steps of 0.1. Dedicated UnZip software is recommended for accessing the dataset, for example, IZArc.en_GB
dc.description.tableofcontentsThe dataset contains landscape data for "Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study", G. Ochoa, N. Veerapen, F. Daolio, M. Tomassini. The 17th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2017), 19-21 April 2017, Amsterdam, The Netherlands. The dataset describes the network structure of the local optima networks (LON) for Number Partitioning Problem (NPP) instances of size 10, 15 and 20, and with values of parameter k from 0.4 to 1.2 in steps of 0.1. The files are compressed within npp_phasetransitions_lon.zip and details on how to interpret the contents are given in readme.txt.en_GB
dc.publisherUniversity of Stirling. Faculty of Natural Sciences.en_GB
dc.relationVeerapen, N; Daolio, F; Ochoa, G; Tomassini, M (2017): Data from "Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study". University of Stirling. Faculty of Natural Sciences. Dataset. http://hdl.handle.net/11667/85en_GB
dc.relation.isreferencedbyVeerapen, N., Daolio, F., Ochoa, G. and Tomassini, M. (2016) Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study, In: Bin H, Lopez-Ibanez M (ed.) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2017, Cham, Switzerland: Springer. 17th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP), 19.4.2017 - 21.4.2017, Amsterdam, The Netherlands. DOI: https://doi.org/10.1007/978-3-319-55453-2_16 Available from: http://hdl.handle.net/1893/24819en_GB
dc.rightsRights covered by the standard CC-BY 4.0 licence: https://creativecommons.org/licenses/by/4.0/en_GB
dc.subjectLocal Optima Networken_GB
dc.subjectNumber Partitioning Problemen_GB
dc.subjectFitness Landscapeen_GB
dc.subjectLocal Searchen_GB
dc.subject.classification::Information and communication technologiesen_GB
dc.subject.classification::Information and communication technologies::Artificial Intelligence Technologies::Meta Heuristicsen_GB
dc.subject.classification::Information and communication technologies::Artificial Intelligence Technologies::Optimisation (AI)en_GB
dc.titleData from "Understanding Phase Transitions with Local Optima Networks: Number Partitioning as a Case Study"en_GB
dc.typedataseten_GB
dc.contributor.emailnve@cs.stir.ac.uken_GB
dc.identifier.projectidRPG-2015-395en_GB
dc.identifier.projectidEP/J017515/1en_GB
dc.title.projectThe Cartography of Computational Search Spacesen_GB
dc.title.projectDAASE: Dynamic Adaptive Automated Software Engineeringen_GB
dc.contributor.affiliationUniversity of Stirling (Computing Science - CSM Dept)en_GB
dc.contributor.affiliationUniversity of Lausanneen_GB
dc.date.publicationyear2017en_GB
dc.identifier.wtid418227-
dc.identifier.wtid415580-
Appears in Collections:University of Stirling Research Data

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