A Unified Conceptual Framework for Geographical Optimization Using Evolutionary Algorithms.
During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographical analysis, however, has been conducted in a problem-specific manner, which prevents an EA designed for one type of problem from being...
| Publicado en: | Annals of the Association of American Geographers Vol. 98; no. 4; pp. 795 - 818 |
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| Formato: | Artículo |
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Taylor & Francis Ltd
December 2008
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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=511430611&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511430611 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 2008 vid: 98 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 511430611 10.1080/00045600802232458 ppf: 795 ppct: 23 formats: tig: atl: A Unified Conceptual Framework for Geographical Optimization Using Evolutionary Algorithms. aug: au: Xiao, Ningchuan su: Geography -- Methodology Geography -- Statistical methods Algorithms Decision making Geography sug: subj: Geography -- Methodology Geography -- Statistical methods Algorithms Decision making Geography ab: During the last two decades, evolutionary algorithms (EAs) have been applied to a wide range of optimization and decision-making problems. Work on EAs for geographical analysis, however, has been conducted in a problem-specific manner, which prevents an EA designed for one type of problem from being used on others. In this article, a formal, conceptual framework is developed to unify the design and implementation of EAs for many geographical optimization problems. The key element in this framework is a graph representation that defines the spatial structure of a broad range of geographical problems. Based on this representation, four types of geographical optimization problems are discussed and a set of algorithms is developed for problems in each type. These algorithms can be used to support the design and implementation of EAs for geographical optimization. Knowledge specific to geographical optimization problems can also be incorporated into the framework. An example of solving political redistricting problems is used to demonstrate the application of this framework. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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