New soft-computing algorithms in atmospheric physics

This Ph.D. Thesis elaborates and analyzes several hybrid Soft-Computing algorithms for optimization and prediction problems in Atmospheric Physics. The core of the Thesis is a recently developed optimization meta-heuristic, the Coral Reefs Optimization Algorithm (CRO), an evolutionary-based approach...

ver descrição completa

Detalhes bibliográficos
Autor: Salcedo Sanz, Sancho
Formato: tesis doctoral
Fecha de publicación:2019
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:español
OAI Identifier:oai:docta.ucm.es:20.500.14352/17266
Acesso em linha:https://hdl.handle.net/20.500.14352/17266
Access Level:acceso abierto
Palavra-chave:519.688(043.2)
Algoritmos computacionales
Computer algorithms
Física atmosférica
2501 Ciencias de la Atmósfera
id ES_6476f1b6339fe4bbcd830a5cb02181f7
oai_identifier_str oai:docta.ucm.es:20.500.14352/17266
network_acronym_str ES
network_name_str España
repository_id_str
spelling New soft-computing algorithms in atmospheric physicsNuevos algoritmos de soft-computing en física atmosféricaSalcedo Sanz, Sancho519.688(043.2)Algoritmos computacionalesComputer algorithmsFísica atmosférica2501 Ciencias de la AtmósferaThis Ph.D. Thesis elaborates and analyzes several hybrid Soft-Computing algorithms for optimization and prediction problems in Atmospheric Physics. The core of the Thesis is a recently developed optimization meta-heuristic, the Coral Reefs Optimization Algorithm (CRO), an evolutionary-based approach which considers a population of possible solutions to a given optimization problem. It simulates different procedures mimicking real processes occurring in coral reefs in order to evolve the population towards good solutions for the problem. Alternative modifications of this algorithm lead to powerful co-evolution meta-heuristics, such as theCRO-SL, in which Substrates implementing different search procedures are included. Another modification of the algorithm leads to the CRO-SP, which considers Species in the evolutionof the population, and it is able to deal with different encodings within a single population.These approaches are hybridized with other Machine Learning and traditional algorithms such as neural networks or the Analogue Method (AM), to come up with powerful hybrid approaches able to solve hard problems in Atmospheric Physics...Universidad Complutense de MadridGarcía Herrera, Ricardo FranciscoUniversidad Complutense de Madrid20192019-08-0120192019-08-01doctoral thesishttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesisapplication/pdfhttps://hdl.handle.net/20.500.14352/17266reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Españolspaopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/172662026-06-02T12:44:21Z
dc.title.none.fl_str_mv New soft-computing algorithms in atmospheric physics
Nuevos algoritmos de soft-computing en física atmosférica
title New soft-computing algorithms in atmospheric physics
spellingShingle New soft-computing algorithms in atmospheric physics
Salcedo Sanz, Sancho
519.688(043.2)
Algoritmos computacionales
Computer algorithms
Física atmosférica
2501 Ciencias de la Atmósfera
title_short New soft-computing algorithms in atmospheric physics
title_full New soft-computing algorithms in atmospheric physics
title_fullStr New soft-computing algorithms in atmospheric physics
title_full_unstemmed New soft-computing algorithms in atmospheric physics
title_sort New soft-computing algorithms in atmospheric physics
dc.creator.none.fl_str_mv Salcedo Sanz, Sancho
author Salcedo Sanz, Sancho
author_facet Salcedo Sanz, Sancho
author_role author
dc.contributor.none.fl_str_mv García Herrera, Ricardo Francisco
Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 519.688(043.2)
Algoritmos computacionales
Computer algorithms
Física atmosférica
2501 Ciencias de la Atmósfera
topic 519.688(043.2)
Algoritmos computacionales
Computer algorithms
Física atmosférica
2501 Ciencias de la Atmósfera
description This Ph.D. Thesis elaborates and analyzes several hybrid Soft-Computing algorithms for optimization and prediction problems in Atmospheric Physics. The core of the Thesis is a recently developed optimization meta-heuristic, the Coral Reefs Optimization Algorithm (CRO), an evolutionary-based approach which considers a population of possible solutions to a given optimization problem. It simulates different procedures mimicking real processes occurring in coral reefs in order to evolve the population towards good solutions for the problem. Alternative modifications of this algorithm lead to powerful co-evolution meta-heuristics, such as theCRO-SL, in which Substrates implementing different search procedures are included. Another modification of the algorithm leads to the CRO-SP, which considers Species in the evolutionof the population, and it is able to deal with different encodings within a single population.These approaches are hybridized with other Machine Learning and traditional algorithms such as neural networks or the Analogue Method (AM), to come up with powerful hybrid approaches able to solve hard problems in Atmospheric Physics...
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-08-01
2019
2019-08-01
dc.type.none.fl_str_mv doctoral thesis
http://purl.org/coar/resource_type/c_db06
dc.type.openaire.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/17266
url https://hdl.handle.net/20.500.14352/17266
dc.language.none.fl_str_mv Español
spa
language_invalid_str_mv Español
language spa
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad Complutense de Madrid
publisher.none.fl_str_mv Universidad Complutense de Madrid
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869409648339058688
score 15,301603

Registos relacionados