We are analyzing https://link.springer.com/article/10.1007/s10479-011-0841-3.

Title:
Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms | Annals of Operations Research
Description:
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 Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods. MOEA makes it possible to achieve a compromise between spatial econometric methods as it highlights areas where a specific phenomenon shows significantly high autocorrelation. The spatial distribution of financially compromised olive-tree farms in Andalusia (Spain) was selected for analysis and two fuzzy hot-spots were statistically identified and spatially located. Hot-spots can be considered to be spatial fuzzy sets where the spatial units have a membership degree that can also be calculated.
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Topics {✒️}

/bin/portal/dgpagraria/estadisticas/estadisticasagrarias/anuario2004 es/agriculturaypesca/portal/www/portal/ month download article/chapter multi-objective evolutionary algorithm multi-objective evolutionary algorithms multi-objective genetic algorithms solving multi-objective problems evolutionary multi-criterion optimization fuzzy/multiobjective genetic systems financially compromised areas related subjects financially compromised farms hierarchical location-allocation model pareto evolutionary algorithm multi-objective optimization multi objective optimization fuzzy hot-spots interactive genetic algorithm privacy choices/manage cookies multiobjective evolutionary algorithms highly autocorrelated areas demand point aggregation business administration faculty operations research aims spatial fuzzy sets multi-criteria methods genetic search strategies evolutionary engineering design local indicators consejería de agricultura article garcía-alonso operational research book full article pdf multicriterion optimal design direct spatial optimization fuzzy evolutionary computation spatial econometric methods parallel problem solving pareto set generation smallholder dairy farms ieee service center spatial income distribution european economic area linear goal programming technical report ci-60/98 mapping sunflower yield ridolfia segetum patches expected referral distances linear programming examples spatial econometric issues

Schema {🗺️}

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         headline:Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms
         description: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 Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods. MOEA makes it possible to achieve a compromise between spatial econometric methods as it highlights areas where a specific phenomenon shows significantly high autocorrelation. The spatial distribution of financially compromised olive-tree farms in Andalusia (Spain) was selected for analysis and two fuzzy hot-spots were statistically identified and spatially located. Hot-spots can be considered to be spatial fuzzy sets where the spatial units have a membership degree that can also be calculated.
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      description: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 Evolutionary Algorithm v.2) has been designed to evaluate three different fitness functions (fine-grained strength, the weighted sum of objectives and fuzzy evaluation of weighted objectives) and three LISA methods. MOEA makes it possible to achieve a compromise between spatial econometric methods as it highlights areas where a specific phenomenon shows significantly high autocorrelation. The spatial distribution of financially compromised olive-tree farms in Andalusia (Spain) was selected for analysis and two fuzzy hot-spots were statistically identified and spatially located. Hot-spots can be considered to be spatial fuzzy sets where the spatial units have a membership degree that can also be calculated.
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         Theory of Computation
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