Performance Assessment of the Canonical Genetic Algorithm: a Study on Parallel Processing Via GPU Architecture
Genetic Algorithms (GAs) exhibit a well-balanced operation, combining exploration with exploitation. This balance, which has a strong impact on the quality of the solutions, depends on the right choice of the genetic operators and on the size of the population. The results reported in the present wo...
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| Formato: | bookPart |
| Idioma: | eng |
| Publicado em: |
2020
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| Texto completo: | http://hdl.handle.net/10400.6/8205 |
| País: | Portugal |
| Oai: | oai:ubibliorum.ubi.pt:10400.6/8205 |
| Resumo: | Genetic Algorithms (GAs) exhibit a well-balanced operation, combining exploration with exploitation. This balance, which has a strong impact on the quality of the solutions, depends on the right choice of the genetic operators and on the size of the population. The results reported in the present work shows that the GPU architecture is an efficient alternative to implement population-based search methods. In the case of heavy workloads the speedup gains are quite impressive. The reported experiments also show that the two-dimensional granularity offered by the GPU architecture is advantageous for the operators presenting functional and data independence at the population+genotype level. |
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