Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-5820
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dc.contributor.authorSchweim, Dirk-
dc.contributor.authorWittenberg, David-
dc.contributor.authorRothlauf, Franz-
dc.date.accessioned2022-05-03T09:45:51Z-
dc.date.available2022-05-03T09:45:51Z-
dc.date.issued2021-
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/5829-
dc.description.abstractThe initial population in genetic programming (GP) should form a representative sample of all possible solutions (the search space). While large populations accurately approximate the distribution of possible solutions, small populations tend to incorporate a sampling error. This paper analyzes how the size of a GP population affects the sampling error and contributes to answering the question of how to size initial GP populations. First, we present a probabilistic model of the expected number of subtrees for GP populations initialized with full, grow, or ramped half-and-half. Second, based on our frequency model, we present a model that estimates the sampling error for a given GP population size. We validate our models empirically and show that, compared to smaller population sizes, our recommended population sizes largely reduce the sampling error of measured fitness values. Increasing the population sizes even more, however, does not considerably reduce the sampling error of fitness values. Last, we recommend population sizes for some widely used benchmark problem instances that result in a low sampling error. A low sampling error at initialization is necessary (but not sufficient) for a reliable search since lowering the sampling error means that the overall random variations in a random sample are reduced. Our results indicate that sampling error is a severe problem for GP, making large initial population sizes necessary to obtain a low sampling error. Our model allows practitioners of GP to determine a minimum initial population size so that the sampling error is lower than a threshold, given a confidence level.en_GB
dc.language.isoengde
dc.rightsCC BY*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subject.ddc330 Wirtschaftde_DE
dc.subject.ddc330 Economicsen_GB
dc.titleOn sampling error in genetic programmingen_GB
dc.typeZeitschriftenaufsatzde
dc.identifier.doihttp://doi.org/10.25358/openscience-5820-
jgu.type.dinitypearticleen_GB
jgu.type.versionPublished versionde
jgu.type.resourceTextde
jgu.organisation.departmentFB 03 Rechts- und Wirtschaftswissenschaftende
jgu.organisation.number2300-
jgu.organisation.nameJohannes Gutenberg-Universität Mainz-
jgu.rights.accessrightsopenAccess-
jgu.journal.titleNatural computingde
jgu.journal.volume2021de
jgu.publisher.year2021-
jgu.publisher.nameSpringer Science + Business Media B.V.de
jgu.publisher.placeDordrechtde
jgu.publisher.issn1572-9796de
jgu.organisation.placeMainz-
jgu.subject.ddccode330de
jgu.publisher.doi10.1007/s11047-020-09828-wde
Appears in collections:JGU-Publikationen

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