Fluvial restoration by applying multi-target genetic algorithms

Authors

  • Ángel Údias European Commission. Joint Research Centre. Institute for Environment and Sustainability.
  • Andrés Redchuk Facultad Ciencias Empresariales. Universidad Autónoma de Chile.
  • Javier Cano Departamento Estadística e I.O. Universidad Rey Juan Carlos. Madrid.
  • Lorenzo Galbiati Agència Catalana de l’Aigua.

Keywords:

genetic algorithm, multicriteria, cost-efficacy, water framework directive, metrics

Abstract

Analyzing the processes of environmental resource management, it is clear the increased concern of the environment, configured as a factor (in conjunction with economic and technological criteria, etc.) to take into account in decision-making processes. This combination of factors of different nature complicates the quantitative analysis of the alternatives to which the decision maker has to face, especially if one takes into account environmental factors that typically, by their very nature, are often difficult to compare and quantify.

The use of a system of multicriteria decision support that integrate engine optimization as a multiobjective genetic algorithm is suitable to apply to the environmental issues presented in this article.

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References

AGÈNCIA CATALANA DE L'AIGUA (2003): ?ESTUDI DE MODELITZACIÓ I PLANIFICACIÓ DE LA GESTIÓ HÍDRICA DE CATALUNYA?. Maig 2003.

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EUROPEAN COMMISSION (2000): ?DIRECTIVE 2000/60/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL OF 23 OCTOBER 2000?. Establish of a framework for Community action in the field of water policy.

EUROPEAN COMMISSION (2001): ?STRATEGIC DOCUMENT?. Common Strategy on the Implementation of the Water Framework Directive.

EUROPEAN COMMISSION (2002): ?ECONOMICS AND THE ENVIRONMENT?. The implementation challenge of the Water Framework Directive. Policy Summary to the Guidance Document.

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Published

2018-06-14

Issue

Section

Artículos Científicos

How to Cite

Fluvial restoration by applying multi-target genetic algorithms. (2018). Revista De La Escuela De Perfeccionamiento En Investigación Operativa, 21(34), 62-80. https://revistas.unc.edu.ar/index.php/epio/article/view/20306