MCPC and MCMP Evolutionary Algorithms for the TSP

Autores
Carballido, Jessica Andrea; Ponzoni, Ignacio; Brignole, Nélida B.
Año de publicación
2003
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The Travelling Salesman Problem (TSP) is an NP-complete problem that has to be solved with optimisation tools, such as the evolutionary algorithms (EA). There are various ways of representing the TSP tours through EAs. Two of the most often employed genetic representations are the ordinal and path representations. In this work we present several EAs based on those representations combined with different crossover operators, namely Single Crossover Per Couple (SCPC), Multiple Crossover Per Couple (MCPC) and Multiple Crossover with Multiple Parents (MCMP). The implementations were tested for several academic case studies generated at random. The EAs with MCPC exhibited the best performance, while the MCMP variants yielded premature convergence problems.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Travelling Salesman Problem
evolutionary algorithms
Single Crossover Per Couple
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/184871
Acceder
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network_name_str SEDICI (UNLP)
spelling MCPC and MCMP Evolutionary Algorithms for the TSPCarballido, Jessica AndreaPonzoni, IgnacioBrignole, Nélida B.Ciencias InformáticasTravelling Salesman Problemevolutionary algorithmsSingle Crossover Per CoupleThe Travelling Salesman Problem (TSP) is an NP-complete problem that has to be solved with optimisation tools, such as the evolutionary algorithms (EA). There are various ways of representing the TSP tours through EAs. Two of the most often employed genetic representations are the ordinal and path representations. In this work we present several EAs based on those representations combined with different crossover operators, namely Single Crossover Per Couple (SCPC), Multiple Crossover Per Couple (MCPC) and Multiple Crossover with Multiple Parents (MCMP). The implementations were tested for several academic case studies generated at random. The EAs with MCPC exhibited the best performance, while the MCMP variants yielded premature convergence problems.Sociedad Argentina de Informática e Investigación Operativa2003-09info: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/10915/184871enginfo:eu-repo/semantics/altIdentifier/issn/1666-1079info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:31:35Zoai:sedici.unlp.edu.ar:10915/184871Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:31:35.392SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv MCPC and MCMP Evolutionary Algorithms for the TSP
title MCPC and MCMP Evolutionary Algorithms for the TSP
spellingShingle MCPC and MCMP Evolutionary Algorithms for the TSP
Carballido, Jessica Andrea
Ciencias Informáticas
Travelling Salesman Problem
evolutionary algorithms
Single Crossover Per Couple
title_short MCPC and MCMP Evolutionary Algorithms for the TSP
title_full MCPC and MCMP Evolutionary Algorithms for the TSP
title_fullStr MCPC and MCMP Evolutionary Algorithms for the TSP
title_full_unstemmed MCPC and MCMP Evolutionary Algorithms for the TSP
title_sort MCPC and MCMP Evolutionary Algorithms for the TSP
dc.creator.none.fl_str_mv Carballido, Jessica Andrea
Ponzoni, Ignacio
Brignole, Nélida B.
author Carballido, Jessica Andrea
author_facet Carballido, Jessica Andrea
Ponzoni, Ignacio
Brignole, Nélida B.
author_role author
author2 Ponzoni, Ignacio
Brignole, Nélida B.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Travelling Salesman Problem
evolutionary algorithms
Single Crossover Per Couple
topic Ciencias Informáticas
Travelling Salesman Problem
evolutionary algorithms
Single Crossover Per Couple
dc.description.none.fl_txt_mv The Travelling Salesman Problem (TSP) is an NP-complete problem that has to be solved with optimisation tools, such as the evolutionary algorithms (EA). There are various ways of representing the TSP tours through EAs. Two of the most often employed genetic representations are the ordinal and path representations. In this work we present several EAs based on those representations combined with different crossover operators, namely Single Crossover Per Couple (SCPC), Multiple Crossover Per Couple (MCPC) and Multiple Crossover with Multiple Parents (MCMP). The implementations were tested for several academic case studies generated at random. The EAs with MCPC exhibited the best performance, while the MCMP variants yielded premature convergence problems.
Sociedad Argentina de Informática e Investigación Operativa
description The Travelling Salesman Problem (TSP) is an NP-complete problem that has to be solved with optimisation tools, such as the evolutionary algorithms (EA). There are various ways of representing the TSP tours through EAs. Two of the most often employed genetic representations are the ordinal and path representations. In this work we present several EAs based on those representations combined with different crossover operators, namely Single Crossover Per Couple (SCPC), Multiple Crossover Per Couple (MCPC) and Multiple Crossover with Multiple Parents (MCMP). The implementations were tested for several academic case studies generated at random. The EAs with MCPC exhibited the best performance, while the MCMP variants yielded premature convergence problems.
publishDate 2003
dc.date.none.fl_str_mv 2003-09
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
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/184871
url http://sedici.unlp.edu.ar/handle/10915/184871
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1666-1079
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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