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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/24895
Ver los metadatos del registro completo
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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 |
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http://sedici.unlp.edu.ar/handle/10915/24895 |
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http://sedici.unlp.edu.ar/handle/10915/24895 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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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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