Exploring the Geographic Consequences of Public Policies Using Evolutionary Algorithms.
The writers present a technique based on an evolutionary algorithm (EA) that generates a large number of optimal and near-optimal solutions to a broad range of land management problems. As a context to their new technique, they explore the effect of the U.S. Department of Agriculture's Conservation...
| Publicado en: | Annals of the Association of American Geographers Vol. 94; no. 4; pp. 827 - 848 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
December 2004
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=513182576&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513182576 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00045608 AAG jtl: Annals of the Association of American Geographers issn: 00045608 maglogo: N pubinfo: dt: December 2004 vid: 94 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 513182576 ppf: 827 ppct: 21 formats: tig: atl: Exploring the Geographic Consequences of Public Policies Using Evolutionary Algorithms. aug: au: Bennett, David A. Xiao, Ningchuan Armstrong, Marc P. su: Algorithms Geography -- Methodology Geography -- Statistical methods Decision making Government policy Agricultural policy United States sug: subj: United States Algorithms Geography -- Methodology Geography -- Statistical methods Decision making Government policy Agricultural policy ab: The writers present a technique based on an evolutionary algorithm (EA) that generates a large number of optimal and near-optimal solutions to a broad range of land management problems. As a context to their new technique, they explore the effect of the U.S. Department of Agriculture's Conservation Reserve Program on rural landscapes. They assume three objectives: maximize farm income, maximize environmental quality, and minimize public investment in conservation programs. They develop analytical and visualization tools to lessen the burden associated with exploring the large number of solutions that are produced by this method. They reveal that the EA-based approach can generate results equal to and significantly more diverse than conventional integer programming techniques. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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