Parallel hybrid evolutionary algorithms on GPU

The Van Luong, Nouredine Melab, El-Ghazali Talbi
<span title="">2010</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2dmkezwdzrcofn7zdzzte2h75i" style="color: black;">IEEE Congress on Evolutionary Computation</a> </i> &nbsp;
Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of methods such as evolutionary algorithms and local searches have provided very powerful search algorithms. However, due to their complexity, the computational time of the solution search exploration remains exorbitant when large problem instances are to be solved. Therefore, the use of GPUbased parallel computing is required as a complementary way to speed up the search.
more &raquo; ... s paper presents a new methodology to design and implement efficiently and effectively hybrid evolutionary algorithms on GPU accelerators. The methodology enables efficient mappings of the explored search space onto the GPU memory hierarchy. The experimental results show that the approach is very efficient especially for large problem instances.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cec.2010.5586403">doi:10.1109/cec.2010.5586403</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cec/LuongMT10.html">dblp:conf/cec/LuongMT10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d5f3jph2kza4nc2xsqfqvw2pny">fatcat:d5f3jph2kza4nc2xsqfqvw2pny</a> </span>
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