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Advisor(s)
Abstract(s)
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.
Description
Keywords
Data parallelism GPGPU Parallel Genetic Algorithms OpenCL