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Many-threaded Differential Evolution on the GPU

Pavel Kromer, Jan Platos, Vaclav Snasel, Ajith Abraham
IT4Innovations, VSB – Technical University of Ostrava, 17. listopadu 12, Ostrava, Czech Republic
Chapter in the book "Massively Parallel Evolutionary Computation on GPGPUs", Springer Verlag, 2013

@article{kromer2013many,

   title={Many-threaded Differential Evolution on the GPU},

   author={Kr{"o}mer, P. and Plato{v{s}}, J. and Sn{‘a}{v{s}}el, V. and Abraham, A.},

   year={2013}

}

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Differential evolution (DE) is an efficient populational meta-heuristic optimization algorithm that has been applied to many difficult real world problems. Due to the relative simplicity of its operations and real encoded data structures, it is very suitable for a parallel implementation on multicore systems and on the GPUs that nowadays reach peak performance of hundreds and thousands of giga FLOPS (floating-point operations per second). In this chapter, we present a simple yet highly parallel implementation of the differential evolution on the GPU using the CUDA architecture and demonstrate its performance on selected test problems.
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