Studying the parallel task scheduling problem with conventional and evolutionary algorithms

This work summarizes results when facing the problem of allocating a number of nonidentical tasks in a parallel system. The model assumes that the system consists of a number of identical processors and that only one task may be executed on a processor at a time. All schedules and tasks are non-pree...

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Detalles Bibliográficos
Autores principales: Gatica, Claudia Ruth, Esquivel, Susana Cecilia, Gallard, Raúl Hector
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2001
Materias:
Ciencias Informáticas
parallel task
Parallel
Scheduling
conventional and evolutionary algorithms
Algorithms
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/21673
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-21673
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
parallel task
Parallel
Scheduling
conventional and evolutionary algorithms
Algorithms
spellingShingle Ciencias Informáticas
parallel task
Parallel
Scheduling
conventional and evolutionary algorithms
Algorithms
Gatica, Claudia Ruth
Esquivel, Susana Cecilia
Gallard, Raúl Hector
Studying the parallel task scheduling problem with conventional and evolutionary algorithms
topic_facet Ciencias Informáticas
parallel task
Parallel
Scheduling
conventional and evolutionary algorithms
Algorithms
description This work summarizes results when facing the problem of allocating a number of nonidentical tasks in a parallel system. The model assumes that the system consists of a number of identical processors and that only one task may be executed on a processor at a time. All schedules and tasks are non-preemptive. Graham’s [8] well-known list scheduling algorithm (LSA) was contrasted with different evolutionary algorithms (EAs), which differ on the representations and the recombinative approach used. Regarding the representation, direct and indirect representations of schedules were used. Concerning recombination, the conventional single crossover per couple (SCPC), and multiple crossovers per couple (MCPC) [3], [4] were implemented. Latest improvements in evolutionary computation include multirecombinative variants. Multiple crossovers multiples on parents (MCMP) provides a means to exploit good features of more than two parents selected according to their fitness by repeatedly applying any crossover method: a number prq of crossovers is applied on a number sut of selected parents. Performance enhancements were clearly demonstrated in single and multicriteria optimisation [5], [6] under this approach. The use of a stud is a well-known practice in breeding by which a breeding animal due to its special features is selected more often for reproduction. This model of reproduction is being implemented for the Parallel Task Scheduling Problem.
format Objeto de conferencia
Objeto de conferencia
author Gatica, Claudia Ruth
Esquivel, Susana Cecilia
Gallard, Raúl Hector
author_facet Gatica, Claudia Ruth
Esquivel, Susana Cecilia
Gallard, Raúl Hector
author_sort Gatica, Claudia Ruth
title Studying the parallel task scheduling problem with conventional and evolutionary algorithms
title_short Studying the parallel task scheduling problem with conventional and evolutionary algorithms
title_full Studying the parallel task scheduling problem with conventional and evolutionary algorithms
title_fullStr Studying the parallel task scheduling problem with conventional and evolutionary algorithms
title_full_unstemmed Studying the parallel task scheduling problem with conventional and evolutionary algorithms
title_sort studying the parallel task scheduling problem with conventional and evolutionary algorithms
publishDate 2001
url http://sedici.unlp.edu.ar/handle/10915/21673
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AT esquivelsusanacecilia studyingtheparalleltaskschedulingproblemwithconventionalandevolutionaryalgorithms
AT gallardraulhector studyingtheparalleltaskschedulingproblemwithconventionalandevolutionaryalgorithms
bdutipo_str Repositorios
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