Speeding up DSMGA-II on CUDA platform
Journal
GECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference
Pages
809-816
Date Issued
2017
Author(s)
Li, S.-C.
Abstract
This paper proposes two CUDA based implementations to speed up the model building process for DSMGA-II, which has shown superior optimization ability to hBOA and LT-GOMEA on various benchmark problems. The first implementation is lossless, which is algorithmically identical to the original version. The second implementation is lossy, which sacrifices some accuracy for further speedup. On several commonly used benchmark problems, the proposed implementations are stable. As the problems become larger, the amount of speedup increases accordingly. The lossless scheme speeds up the first part of model building for more than ten times; the lossy implementation further speeds up the second part of model building for more than 400 times on a 600-bit folded-trap problem. The limitation of such implementations are also discussed in detail in this paper.
SDGs
Type
conference paper