Evolutionary Algorithms and Agricultural Systems [electronic resource] /

Evolutionary Algorithms and Agricultural Systems deals with the practical application of evolutionary algorithms to the study and management of agricultural systems. The rationale of systems research methodology is introduced, and examples listed of real-world applications. It is the integration of these agricultural systems models with optimization techniques, primarily genetic algorithms, which forms the focus of this book. The advantages are outlined, with examples of agricultural models ranging from national and industry-wide studies down to the within-farm scale. The potential problems of this approach are also discussed, along with practical methods of resolving these problems. Agricultural applications using alternate optimization techniques (gradient and direct-search methods, simulated annealing and quenching, and the tabu search strategy) are also listed and discussed. The particular problems and methodologies of these algorithms, including advantageous features that may benefit a hybrid approach or be usefully incorporated into evolutionary algorithms, are outlined. From consideration of this and the published examples, it is concluded that evolutionary algorithms are the superior method for the practical optimization of models of agricultural and natural systems. General recommendations on robust options and parameter settings for evolutionary algorithms are given for use in future studies. Evolutionary Algorithms and Agricultural Systems will prove useful to practitioners and researchers applying these methods to the optimization of agricultural or natural systems, and would also be suited as a text for systems management, applied modeling, or operations research.

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Bibliographic Details
Main Authors: Mayer, David G. author., SpringerLink (Online service)
Format: Texto biblioteca
Language:eng
Published: Boston, MA : Springer US : Imprint: Springer, 2002
Subjects:Computer science., Computers., Artificial intelligence., Agriculture., Calculus of variations., Computer Science., Artificial Intelligence (incl. Robotics)., Theory of Computation., Calculus of Variations and Optimal Control; Optimization.,
Online Access:http://dx.doi.org/10.1007/978-1-4615-1717-7
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record_format koha
institution COLPOS
collection Koha
country México
countrycode MX
component Bibliográfico
access En linea
En linea
databasecode cat-colpos
tag biblioteca
region America del Norte
libraryname Departamento de documentación y biblioteca de COLPOS
language eng
topic Computer science.
Computers.
Artificial intelligence.
Agriculture.
Calculus of variations.
Computer Science.
Artificial Intelligence (incl. Robotics).
Theory of Computation.
Calculus of Variations and Optimal Control; Optimization.
Agriculture.
Computer science.
Computers.
Artificial intelligence.
Agriculture.
Calculus of variations.
Computer Science.
Artificial Intelligence (incl. Robotics).
Theory of Computation.
Calculus of Variations and Optimal Control; Optimization.
Agriculture.
spellingShingle Computer science.
Computers.
Artificial intelligence.
Agriculture.
Calculus of variations.
Computer Science.
Artificial Intelligence (incl. Robotics).
Theory of Computation.
Calculus of Variations and Optimal Control; Optimization.
Agriculture.
Computer science.
Computers.
Artificial intelligence.
Agriculture.
Calculus of variations.
Computer Science.
Artificial Intelligence (incl. Robotics).
Theory of Computation.
Calculus of Variations and Optimal Control; Optimization.
Agriculture.
Mayer, David G. author.
SpringerLink (Online service)
Evolutionary Algorithms and Agricultural Systems [electronic resource] /
description Evolutionary Algorithms and Agricultural Systems deals with the practical application of evolutionary algorithms to the study and management of agricultural systems. The rationale of systems research methodology is introduced, and examples listed of real-world applications. It is the integration of these agricultural systems models with optimization techniques, primarily genetic algorithms, which forms the focus of this book. The advantages are outlined, with examples of agricultural models ranging from national and industry-wide studies down to the within-farm scale. The potential problems of this approach are also discussed, along with practical methods of resolving these problems. Agricultural applications using alternate optimization techniques (gradient and direct-search methods, simulated annealing and quenching, and the tabu search strategy) are also listed and discussed. The particular problems and methodologies of these algorithms, including advantageous features that may benefit a hybrid approach or be usefully incorporated into evolutionary algorithms, are outlined. From consideration of this and the published examples, it is concluded that evolutionary algorithms are the superior method for the practical optimization of models of agricultural and natural systems. General recommendations on robust options and parameter settings for evolutionary algorithms are given for use in future studies. Evolutionary Algorithms and Agricultural Systems will prove useful to practitioners and researchers applying these methods to the optimization of agricultural or natural systems, and would also be suited as a text for systems management, applied modeling, or operations research.
format Texto
topic_facet Computer science.
Computers.
Artificial intelligence.
Agriculture.
Calculus of variations.
Computer Science.
Artificial Intelligence (incl. Robotics).
Theory of Computation.
Calculus of Variations and Optimal Control; Optimization.
Agriculture.
author Mayer, David G. author.
SpringerLink (Online service)
author_facet Mayer, David G. author.
SpringerLink (Online service)
author_sort Mayer, David G. author.
title Evolutionary Algorithms and Agricultural Systems [electronic resource] /
title_short Evolutionary Algorithms and Agricultural Systems [electronic resource] /
title_full Evolutionary Algorithms and Agricultural Systems [electronic resource] /
title_fullStr Evolutionary Algorithms and Agricultural Systems [electronic resource] /
title_full_unstemmed Evolutionary Algorithms and Agricultural Systems [electronic resource] /
title_sort evolutionary algorithms and agricultural systems [electronic resource] /
publisher Boston, MA : Springer US : Imprint: Springer,
publishDate 2002
url http://dx.doi.org/10.1007/978-1-4615-1717-7
work_keys_str_mv AT mayerdavidgauthor evolutionaryalgorithmsandagriculturalsystemselectronicresource
AT springerlinkonlineservice evolutionaryalgorithmsandagriculturalsystemselectronicresource
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spelling KOHA-OAI-TEST:2174312018-07-30T23:53:25ZEvolutionary Algorithms and Agricultural Systems [electronic resource] / Mayer, David G. author. SpringerLink (Online service) textBoston, MA : Springer US : Imprint: Springer,2002.engEvolutionary Algorithms and Agricultural Systems deals with the practical application of evolutionary algorithms to the study and management of agricultural systems. The rationale of systems research methodology is introduced, and examples listed of real-world applications. It is the integration of these agricultural systems models with optimization techniques, primarily genetic algorithms, which forms the focus of this book. The advantages are outlined, with examples of agricultural models ranging from national and industry-wide studies down to the within-farm scale. The potential problems of this approach are also discussed, along with practical methods of resolving these problems. Agricultural applications using alternate optimization techniques (gradient and direct-search methods, simulated annealing and quenching, and the tabu search strategy) are also listed and discussed. The particular problems and methodologies of these algorithms, including advantageous features that may benefit a hybrid approach or be usefully incorporated into evolutionary algorithms, are outlined. From consideration of this and the published examples, it is concluded that evolutionary algorithms are the superior method for the practical optimization of models of agricultural and natural systems. General recommendations on robust options and parameter settings for evolutionary algorithms are given for use in future studies. Evolutionary Algorithms and Agricultural Systems will prove useful to practitioners and researchers applying these methods to the optimization of agricultural or natural systems, and would also be suited as a text for systems management, applied modeling, or operations research.Rationale for Systems Modelling -- Agricultural Systems Models -- Application of Evolutionary Algorithms to Models -- Applications of Alternate Optimisation Techniques -- Comparisons of Optimisation Techniques -- Robust Parameters for Evolutionary Algorithms -- Future Developments -- Appendix 1 -- References.Evolutionary Algorithms and Agricultural Systems deals with the practical application of evolutionary algorithms to the study and management of agricultural systems. The rationale of systems research methodology is introduced, and examples listed of real-world applications. It is the integration of these agricultural systems models with optimization techniques, primarily genetic algorithms, which forms the focus of this book. The advantages are outlined, with examples of agricultural models ranging from national and industry-wide studies down to the within-farm scale. The potential problems of this approach are also discussed, along with practical methods of resolving these problems. Agricultural applications using alternate optimization techniques (gradient and direct-search methods, simulated annealing and quenching, and the tabu search strategy) are also listed and discussed. The particular problems and methodologies of these algorithms, including advantageous features that may benefit a hybrid approach or be usefully incorporated into evolutionary algorithms, are outlined. From consideration of this and the published examples, it is concluded that evolutionary algorithms are the superior method for the practical optimization of models of agricultural and natural systems. General recommendations on robust options and parameter settings for evolutionary algorithms are given for use in future studies. Evolutionary Algorithms and Agricultural Systems will prove useful to practitioners and researchers applying these methods to the optimization of agricultural or natural systems, and would also be suited as a text for systems management, applied modeling, or operations research.Computer science.Computers.Artificial intelligence.Agriculture.Calculus of variations.Computer Science.Artificial Intelligence (incl. Robotics).Theory of Computation.Calculus of Variations and Optimal Control; Optimization.Agriculture.Springer eBookshttp://dx.doi.org/10.1007/978-1-4615-1717-7URN:ISBN:9781461517177