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dc.contributor.authorCaraffini, Fabioen
dc.contributor.authorNeri, Ferranteen
dc.contributor.authorIacca, Giovannien
dc.date.accessioned2017-03-08T10:54:10Z
dc.date.available2017-03-08T10:54:10Z
dc.date.issued2017-01
dc.identifier.citationCaraffini, F., Neri, F. and Iacca, G. (2017) Large Scale Problems in Practice: The effect of dimensionality on the interaction among variables. In: Squillero G., Sim K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science, vol 10199, pp. 636-652en
dc.identifier.urihttp://hdl.handle.net/2086/13465
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractThis article performs a study on correlation between pairs of variables in dependence on the problem dimensionality. Two tests, based on Pearson and Spearman coefficients, have been designed and used in this work. In total, $86$ test problems ranging between 10 and 1000 variables have been studied. If the most commonly used experimental conditions are used, the correlation between pairs of variables appears, from the perspective of the search algorithm, to consistently decrease. This effect is not due to the fact that the dimensionality modifies the nature of the problem but is a consequence of the experimental conditions: the computational feasibility of the experiments imposes an extremely shallow search in case of high dimensions. An exponential increase of budget and population with the dimensionality is still practically impossible. Nonetheless, since real-world application may require that large scale problems are tackled despite of the limited budget, an algorithm can quickly improve upon initial guesses if it integrates the knowledge that an apparent weak correlation between pairs of variables occurs, regardless the nature of the problem.en
dc.language.isoenen
dc.subjectLarge Scale Optimisationen
dc.subjectScalabilityen
dc.titleLarge Scale Problems in Practice: The effect of dimensionality on the interaction among variablesen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-55849-3_41
dc.researchgroupCentre for Computational Intelligence
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-01-11en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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