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Heavy-tailed distributions in combinatorial search

dc.contributor.authorGomes, Carla P.
dc.contributor.authorSelman, Bart
dc.contributor.authorCrato, Nuno
dc.date.accessioned2023-05-09T09:45:52Z
dc.date.available2023-05-09T09:45:52Z
dc.date.issued1997
dc.description.abstractCombinatorial search methods often exhibit a large variability in performance. We study the cost profiles of combinatorial search procedures. Our study reveals some intriguing properties of such cost profiles. The distributions are often characterized by very long tails or "heavy tails". We will show that these distributions are best characterized by a general class of distributions that have no moments (i.e., an infinite mean, variance, etc.). Such non-standard distributions have recently been observed in areas as diverse as economics, statistical physics, and geophysics. They are closely related to fractal phenomena, whose study was introduced by Mandelbrot. We believe this is the first finding of these distributions in a purely computational setting. We also show how random restarts can effectively eliminate heavy-tailed behavior, thereby dramatically improving the overall performance of a search procedure.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGomes, Carla P.; Bart Selman and Nuno Crato .(1997). “Heavy-tailed distributions in combinatorial search”. in Principles and Practice of Constraint Programming-CP . Smolka (Ed.), Springer Lecture Notes in Computer Science 1330: pp. 121-135. (Search PDF in 2023).pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/27732
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.subjectEconomicspt_PT
dc.subjectStatisticcal Physicspt_PT
dc.subjectGeophysicspt_PT
dc.subjectDifferent Heuristicspt_PT
dc.subjectStochastic Methodspt_PT
dc.titleHeavy-tailed distributions in combinatorial searchpt_PT
dc.typebook part
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typebookPartpt_PT

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