Multiple parents, multiple crossovers and incest prevention in evolutionary computation
- Autores
- Alfonso, Hugo; Minetti, Gabriela F.; Salto, Carolina; Gallard, Raúl Hector
- Año de publicación
- 1999
- Idioma
- español castellano
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Multimodal optimization is an always present topic in Computer Systems and Networks design and implementation. - Evolutionary Computation is an emergent field which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. The present contribution gives an insight of the current enhancements that can be done in evolutionary techniques, attempting to balance exploitation and explotation to avoid premature convergence during the search process. Multiple parents, multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: The set of suboptimal solutions are concentrated nearby the optimal solution. This paper shows the design, implementation and partial performance results when a combination of multiple crossovers on multiple parents and incest prevention is applied to an evolutionary algorithm optimizing two difficult multimodal functions.
Eje: Redes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
multiple crossovers
Multiple parents
incest prevention
evolutionary computation
ARTIFICIAL INTELLIGENCE - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22221
Ver los metadatos del registro completo
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Multiple parents, multiple crossovers and incest prevention in evolutionary computationAlfonso, HugoMinetti, Gabriela F.Salto, CarolinaGallard, Raúl HectorCiencias Informáticasmultiple crossoversMultiple parentsincest preventionevolutionary computationARTIFICIAL INTELLIGENCEMultimodal optimization is an always present topic in Computer Systems and Networks design and implementation. - Evolutionary Computation is an emergent field which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. The present contribution gives an insight of the current enhancements that can be done in evolutionary techniques, attempting to balance exploitation and explotation to avoid premature convergence during the search process. Multiple parents, multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: The set of suboptimal solutions are concentrated nearby the optimal solution. This paper shows the design, implementation and partial performance results when a combination of multiple crossovers on multiple parents and incest prevention is applied to an evolutionary algorithm optimizing two difficult multimodal functions.Eje: Redes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)1999-05info: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/10915/22221spainfo: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:UNLP2025-09-03T10:27:47Zoai:sedici.unlp.edu.ar:10915/22221Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:27:47.7SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Multiple parents, multiple crossovers and incest prevention in evolutionary computation |
title |
Multiple parents, multiple crossovers and incest prevention in evolutionary computation |
spellingShingle |
Multiple parents, multiple crossovers and incest prevention in evolutionary computation Alfonso, Hugo Ciencias Informáticas multiple crossovers Multiple parents incest prevention evolutionary computation ARTIFICIAL INTELLIGENCE |
title_short |
Multiple parents, multiple crossovers and incest prevention in evolutionary computation |
title_full |
Multiple parents, multiple crossovers and incest prevention in evolutionary computation |
title_fullStr |
Multiple parents, multiple crossovers and incest prevention in evolutionary computation |
title_full_unstemmed |
Multiple parents, multiple crossovers and incest prevention in evolutionary computation |
title_sort |
Multiple parents, multiple crossovers and incest prevention in evolutionary computation |
dc.creator.none.fl_str_mv |
Alfonso, Hugo Minetti, Gabriela F. Salto, Carolina Gallard, Raúl Hector |
author |
Alfonso, Hugo |
author_facet |
Alfonso, Hugo Minetti, Gabriela F. Salto, Carolina Gallard, Raúl Hector |
author_role |
author |
author2 |
Minetti, Gabriela F. Salto, Carolina Gallard, Raúl Hector |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas multiple crossovers Multiple parents incest prevention evolutionary computation ARTIFICIAL INTELLIGENCE |
topic |
Ciencias Informáticas multiple crossovers Multiple parents incest prevention evolutionary computation ARTIFICIAL INTELLIGENCE |
dc.description.none.fl_txt_mv |
Multimodal optimization is an always present topic in Computer Systems and Networks design and implementation. - Evolutionary Computation is an emergent field which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. The present contribution gives an insight of the current enhancements that can be done in evolutionary techniques, attempting to balance exploitation and explotation to avoid premature convergence during the search process. Multiple parents, multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: The set of suboptimal solutions are concentrated nearby the optimal solution. This paper shows the design, implementation and partial performance results when a combination of multiple crossovers on multiple parents and incest prevention is applied to an evolutionary algorithm optimizing two difficult multimodal functions. Eje: Redes y sistemas inteligentes Red de Universidades con Carreras en Informática (RedUNCI) |
description |
Multimodal optimization is an always present topic in Computer Systems and Networks design and implementation. - Evolutionary Computation is an emergent field which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. The present contribution gives an insight of the current enhancements that can be done in evolutionary techniques, attempting to balance exploitation and explotation to avoid premature convergence during the search process. Multiple parents, multiple crossovers and incest prevention are three different techniques that when combined showed a substantial benefit: The set of suboptimal solutions are concentrated nearby the optimal solution. This paper shows the design, implementation and partial performance results when a combination of multiple crossovers on multiple parents and incest prevention is applied to an evolutionary algorithm optimizing two difficult multimodal functions. |
publishDate |
1999 |
dc.date.none.fl_str_mv |
1999-05 |
dc.type.none.fl_str_mv |
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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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/22221 |
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language |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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