A runnable functional formal memetic algorithm framework

Autores
Krasnogor, Natalio; Mocciola, Pablo Andrés; Pelta, David Alejandro; Ruiz, Germán Esteban; Russo, Wanda Mariana
Año de publicación
1998
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Historically Functional Programming FP for short has been associated with a small scope of applications mainly academic The computer science community did not pay enough attention to its potential perhaps due to the lack of e ciency of functional languages Now new theoretical developments in the eld of FP are emerging and better languages e g Haskell Concurrent and Parallel Haskell have been de ned and implemented Genetic algorithms GA are search and optimization techniques which work on a nature inspired principle the Darwinian evolution The corner idea of Darwin theory is that of natural selection The concept of natural selection is captured by GA Speci cally solutions to a given problem are codi ed in the so called chromosomes The evolution of chromosomes due to the action of crossover mutation and natural selection is simulated through computer code GA have been broadly applied and recognized as a robust search and optimization technique GA combined with a local search stage were called Memetic Algorithms after In this paper a functional framework for formal memetic algorithms is intro duced It can be easily extended by subclassi cation of the class hierarchy to provide genetic algorithm specialization memetic algorithm genetic algorithm with islands of possible solutions etc and additional genetic operators behavior To run the frame work over a particular problem a proper encoding of chromosomes should be provided with an instantiation of the genetic operators We claim that functional programming languages at least the one in which our framework has been developed Haskell have reached the necessary maturity to deal with combinatorial optimization problems
Eje: Teoría
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Informática
Functional Programming
Memetic Algorithm
Combinatorial Optimization
Optimization
Algorithms
Frameworks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/24895

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network_name_str SEDICI (UNLP)
spelling A runnable functional formal memetic algorithm frameworkKrasnogor, NatalioMocciola, Pablo AndrésPelta, David AlejandroRuiz, Germán EstebanRusso, Wanda MarianaCiencias InformáticasInformáticaFunctional ProgrammingMemetic AlgorithmCombinatorial OptimizationOptimizationAlgorithmsFrameworksHistorically Functional Programming FP for short has been associated with a small scope of applications mainly academic The computer science community did not pay enough attention to its potential perhaps due to the lack of e ciency of functional languages Now new theoretical developments in the eld of FP are emerging and better languages e g Haskell Concurrent and Parallel Haskell have been de ned and implemented Genetic algorithms GA are search and optimization techniques which work on a nature inspired principle the Darwinian evolution The corner idea of Darwin theory is that of natural selection The concept of natural selection is captured by GA Speci cally solutions to a given problem are codi ed in the so called chromosomes The evolution of chromosomes due to the action of crossover mutation and natural selection is simulated through computer code GA have been broadly applied and recognized as a robust search and optimization technique GA combined with a local search stage were called Memetic Algorithms after In this paper a functional framework for formal memetic algorithms is intro duced It can be easily extended by subclassi cation of the class hierarchy to provide genetic algorithm specialization memetic algorithm genetic algorithm with islands of possible solutions etc and additional genetic operators behavior To run the frame work over a particular problem a proper encoding of chromosomes should be provided with an instantiation of the genetic operators We claim that functional programming languages at least the one in which our framework has been developed Haskell have reached the necessary maturity to deal with combinatorial optimization problemsEje: TeoríaRed de Universidades con Carreras en Informática (RedUNCI)1998-10info: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/10915/24895enginfo: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:UNLP2025-09-10T11:59:24Zoai:sedici.unlp.edu.ar:10915/24895Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-10 11:59:25.058SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A runnable functional formal memetic algorithm framework
title A runnable functional formal memetic algorithm framework
spellingShingle A runnable functional formal memetic algorithm framework
Krasnogor, Natalio
Ciencias Informáticas
Informática
Functional Programming
Memetic Algorithm
Combinatorial Optimization
Optimization
Algorithms
Frameworks
title_short A runnable functional formal memetic algorithm framework
title_full A runnable functional formal memetic algorithm framework
title_fullStr A runnable functional formal memetic algorithm framework
title_full_unstemmed A runnable functional formal memetic algorithm framework
title_sort A runnable functional formal memetic algorithm framework
dc.creator.none.fl_str_mv Krasnogor, Natalio
Mocciola, Pablo Andrés
Pelta, David Alejandro
Ruiz, Germán Esteban
Russo, Wanda Mariana
author Krasnogor, Natalio
author_facet Krasnogor, Natalio
Mocciola, Pablo Andrés
Pelta, David Alejandro
Ruiz, Germán Esteban
Russo, Wanda Mariana
author_role author
author2 Mocciola, Pablo Andrés
Pelta, David Alejandro
Ruiz, Germán Esteban
Russo, Wanda Mariana
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Informática
Functional Programming
Memetic Algorithm
Combinatorial Optimization
Optimization
Algorithms
Frameworks
topic Ciencias Informáticas
Informática
Functional Programming
Memetic Algorithm
Combinatorial Optimization
Optimization
Algorithms
Frameworks
dc.description.none.fl_txt_mv Historically Functional Programming FP for short has been associated with a small scope of applications mainly academic The computer science community did not pay enough attention to its potential perhaps due to the lack of e ciency of functional languages Now new theoretical developments in the eld of FP are emerging and better languages e g Haskell Concurrent and Parallel Haskell have been de ned and implemented Genetic algorithms GA are search and optimization techniques which work on a nature inspired principle the Darwinian evolution The corner idea of Darwin theory is that of natural selection The concept of natural selection is captured by GA Speci cally solutions to a given problem are codi ed in the so called chromosomes The evolution of chromosomes due to the action of crossover mutation and natural selection is simulated through computer code GA have been broadly applied and recognized as a robust search and optimization technique GA combined with a local search stage were called Memetic Algorithms after In this paper a functional framework for formal memetic algorithms is intro duced It can be easily extended by subclassi cation of the class hierarchy to provide genetic algorithm specialization memetic algorithm genetic algorithm with islands of possible solutions etc and additional genetic operators behavior To run the frame work over a particular problem a proper encoding of chromosomes should be provided with an instantiation of the genetic operators We claim that functional programming languages at least the one in which our framework has been developed Haskell have reached the necessary maturity to deal with combinatorial optimization problems
Eje: Teoría
Red de Universidades con Carreras en Informática (RedUNCI)
description Historically Functional Programming FP for short has been associated with a small scope of applications mainly academic The computer science community did not pay enough attention to its potential perhaps due to the lack of e ciency of functional languages Now new theoretical developments in the eld of FP are emerging and better languages e g Haskell Concurrent and Parallel Haskell have been de ned and implemented Genetic algorithms GA are search and optimization techniques which work on a nature inspired principle the Darwinian evolution The corner idea of Darwin theory is that of natural selection The concept of natural selection is captured by GA Speci cally solutions to a given problem are codi ed in the so called chromosomes The evolution of chromosomes due to the action of crossover mutation and natural selection is simulated through computer code GA have been broadly applied and recognized as a robust search and optimization technique GA combined with a local search stage were called Memetic Algorithms after In this paper a functional framework for formal memetic algorithms is intro duced It can be easily extended by subclassi cation of the class hierarchy to provide genetic algorithm specialization memetic algorithm genetic algorithm with islands of possible solutions etc and additional genetic operators behavior To run the frame work over a particular problem a proper encoding of chromosomes should be provided with an instantiation of the genetic operators We claim that functional programming languages at least the one in which our framework has been developed Haskell have reached the necessary maturity to deal with combinatorial optimization problems
publishDate 1998
dc.date.none.fl_str_mv 1998-10
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/10915/24895
url http://sedici.unlp.edu.ar/handle/10915/24895
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv 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)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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