Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms

Local Indicators of Spatial Aggregation (LISA) can be used as objectives in a multicriteria framework when highly autocorrelated areas (hot-spots) must be identified and geographically located in complex areas. To do so, a Multi-Objective Evolutionary Algorithm (MOEA) based on SPEA2 (Strength Pareto...

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Veröffentlicht in:	Annals of operations research Jg. 219; H. 1; S. 187 - 202
Hauptverfasser:	García-Alonso, Carlos R., Pérez-Naranjo, Leonor M., Fernández-Caballero, Juan C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.08.2014 Springer Springer Nature B.V
Schlagworte:	Algorithms Business and Management Combinatorics Econometrics Economic aspects Evolutionary algorithms Farms Fuzzy Fuzzy logic Fuzzy set theory Fuzzy sets Genetic algorithms Geographic information systems Linear programming Mathematical analysis Objectives Olive Operations research Operations Research/Decision Theory Optimization techniques Spatial analysis Spatial distribution Strength Studies Theory of Computation Spain Andalusia Spain Spatial analysis Fuzzy hot-spots Multiobjective evolutionary algorithms Local indicators of spatial aggregation Financially compromised areas
ISSN:	0254-5330, 1572-9338
Online-Zugang:	Volltext
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