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dc.contributor.authorMcCall, John
dc.contributor.authorChristie, Lee A.
dc.contributor.authorBrownlee, Alexander Edward Ian
dc.date.accessioned2016-03-03T10:14:46Z
dc.date.available2016-03-03T10:14:46Z
dc.date.issued2015
dc.identifier.citationMCCALL, J. A. W., CHRISTIE, L. A. and BROWNLEE, A. E. I, 2015. Generating easy and hard problems using the Proximate Optimality Principle. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO Companion '15). New York: ACM. pp. 767-768.en
dc.identifier.isbn9781450334884en
dc.identifier.urihttp://hdl.handle.net/10059/1406
dc.description.abstractWe present an approach to generating problems of variable difficulty based on the well-known Proximate Optimality Principle (POP), often paraphrased as "similar solutions have similar fitness". We explore definitions of this concept in terms of metrics in objective space and in representation space and define POP in terms of coherence of these metrics. We hypothesise that algorithms will perform well when the neighbourhoods they explore in representation space are coherent with the natural metric induced by fitness on objective space. We develop an explicit method of problem generation which creates bit string problems where the natural fitness metric is coherent or anti-coherent with Hamming neighbourhoods. We conduct experiments to show that coherent problems are easy whereas anti-coherent problems are hard for local hill climbers using the Hamming neighbourhoods.en
dc.language.isoenen
dc.publisherACMen
dc.relation.isreferencedbyCHRISTIE, L. A., 2015. Data set for generating easy and hard problems using the Proximate Optimality Principle. Available from OpenAIR@RGU. [online]. Available from: http://hdl.handle.net/10059/1407en
dc.relation.urihttp://hdl.handle.net/10059/1407
dc.rights© ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO Companion '15) http://doi.acm.org/10.1145/2739482.2764890en
dc.subjectProblem generationen
dc.subjectProximate Optimalityen
dc.subjectEstimation of Distribution Algorithmsen
dc.subjectLandscapesen
dc.titleGenerating easy and hard problems using the Proximate Optimality Principle.en
dc.typeConference publicationsen
dc.publisher.urihttp://doi.acm.org/10.1145/2739482.2764890en


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