Método automático para descoberta de funções de ordenação utilizando programação genética paralela em GPU
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Data
2014-03-28
Autores
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Universidade Federal de Goiás
Resumo
Ranking functions have a vital role in the performance of information retrieval systems
ensuring that documents more related to the user’s search need – represented as a query
– are shown in the top results, preventing the user from having to examine a range of
documents that are not really relevant.
Therefore, this work uses Genetic Programming (GP), an Evolutionary Computation
technique, to find ranking functions automaticaly and systematicaly. Moreover, in this
project the technique of GP was developed following a strategy that exploits parallelism
through graphics processing units.
Other known methods in the context of information retrieval as classification committees
and the Lazy strategy were combined with the proposed approach – called Finch. These
combinations were only feasible due to the GP nature and the use of parallelism.
The experimental results with the Finch, regarding the ranking functions quality, surpassed
the results of several strategies known in the literature. Considering the time performance,
significant gains were also achieved. The solution developed exploiting the
parallelism spends around twenty times less time than the solution using only the central
processing unit.
Descrição
Palavras-chave
Programação genética , Computação paralela , Sistema de recuperação de informação , Ordenação de documentos , Computação evolucionária , CUDA , Unidade gráfica de processamento , Inteligência computacional , Genetic programming , Parallel computing , Information retrieval system , Document ranking , Evolutionary computation , CUDA , Graphics processing unit , Machine learning
Citação
COIMBRA, A. R. Método automático para descoberta de funções de ordenação utilizando programação genética paralela em GPU. 2014. 97 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2014.