Scheduling multiprocessor tasks with genetic algorithms

• Autor(a) principal: Corrêa, Ricardo Cordeiro
• Data de Publicação: 1996
• Outros Autores: Ferreira, Afonso, Rebreyend, Pascal
• Tipo de documento: Relatório
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRJ
• Texto Completo: http://hdl.handle.net/11422/2592
• topic: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO; Multiprocessadores; Escalonamento multidimensional

In the multíprocessor schedulíng problem a given program is to be scheduled in a given multiprocessor system such that the program 's execution time is minimized. This problem being very hard to solve exactly, many heuristic methods for finding a suboptimal schedule exist. We propose a new combined approach, where a genetic algorithm is improved with the introduction of some knowledge about the scheduling problem represented by the use of a list heuristic in the crossover and mutatíon genetic operations. This knowledge-augmented genetic approach is empirically compared with a "pure" genetic algorithm and with a "pure" list heuristic, both from the literature. Results of the experiments carried out with synthetic instances of the scheduling problem show that our knowledge-augmented algorithm produces much better results in terms of quality of solutions, although being slower in terms of execution time.