A phenotypic analysis of three population-based metaheuristics
- Autores
- Orellana, Alina; Minetti, Gabriela F.
- Año de publicación
- 2008
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Metaheuristics are used as very good optimization methods and they imitate natural, biologic, social and cultural process. In this work, we evaluate and compare three different metaheuristics which are population-based: Genetic Algorithms, CHC and Scatter Search. They work with a set of solutions in contrast to trajectory-based metaheuristics which use an only solution. From a comparative analysis, we can infer that Genetic Algorithms and CHC algorithms can solve satisfactorily problems with a growing complexity. While Scatter Search provides high quality solutions but its computational effort is very high too.
Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Heuristic methods
Algorithms
Biology and genetics - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/21765
Ver los metadatos del registro completo
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A phenotypic analysis of three population-based metaheuristicsOrellana, AlinaMinetti, Gabriela F.Ciencias InformáticasHeuristic methodsAlgorithmsBiology and geneticsMetaheuristics are used as very good optimization methods and they imitate natural, biologic, social and cultural process. In this work, we evaluate and compare three different metaheuristics which are population-based: Genetic Algorithms, CHC and Scatter Search. They work with a set of solutions in contrast to trajectory-based metaheuristics which use an only solution. From a comparative analysis, we can infer that Genetic Algorithms and CHC algorithms can solve satisfactorily problems with a growing complexity. While Scatter Search provides high quality solutions but its computational effort is very high too.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2008-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/21765enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:54:47Zoai:sedici.unlp.edu.ar:10915/21765Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:47.319SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
A phenotypic analysis of three population-based metaheuristics |
| title |
A phenotypic analysis of three population-based metaheuristics |
| spellingShingle |
A phenotypic analysis of three population-based metaheuristics Orellana, Alina Ciencias Informáticas Heuristic methods Algorithms Biology and genetics |
| title_short |
A phenotypic analysis of three population-based metaheuristics |
| title_full |
A phenotypic analysis of three population-based metaheuristics |
| title_fullStr |
A phenotypic analysis of three population-based metaheuristics |
| title_full_unstemmed |
A phenotypic analysis of three population-based metaheuristics |
| title_sort |
A phenotypic analysis of three population-based metaheuristics |
| dc.creator.none.fl_str_mv |
Orellana, Alina Minetti, Gabriela F. |
| author |
Orellana, Alina |
| author_facet |
Orellana, Alina Minetti, Gabriela F. |
| author_role |
author |
| author2 |
Minetti, Gabriela F. |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas Heuristic methods Algorithms Biology and genetics |
| topic |
Ciencias Informáticas Heuristic methods Algorithms Biology and genetics |
| dc.description.none.fl_txt_mv |
Metaheuristics are used as very good optimization methods and they imitate natural, biologic, social and cultural process. In this work, we evaluate and compare three different metaheuristics which are population-based: Genetic Algorithms, CHC and Scatter Search. They work with a set of solutions in contrast to trajectory-based metaheuristics which use an only solution. From a comparative analysis, we can infer that Genetic Algorithms and CHC algorithms can solve satisfactorily problems with a growing complexity. While Scatter Search provides high quality solutions but its computational effort is very high too. Workshop de Agentes y Sistemas Inteligentes (WASI) Red de Universidades con Carreras en Informática (RedUNCI) |
| description |
Metaheuristics are used as very good optimization methods and they imitate natural, biologic, social and cultural process. In this work, we evaluate and compare three different metaheuristics which are population-based: Genetic Algorithms, CHC and Scatter Search. They work with a set of solutions in contrast to trajectory-based metaheuristics which use an only solution. From a comparative analysis, we can infer that Genetic Algorithms and CHC algorithms can solve satisfactorily problems with a growing complexity. While Scatter Search provides high quality solutions but its computational effort is very high too. |
| publishDate |
2008 |
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2008-10 |
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info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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eng |
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eng |
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