GPU-based parallel evolution strategy in bioinformatics

The paper “GPU-based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites” (Matthias Leinweber, Thomas Fober, and Bernd Freisleben) presents “a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning.”
The authors applied the SPOT (sequential parameter optimization toolbox) to tune the evolution strategy: “The ES parameters population size, lifetime of individuals, recombination parameter, and mutation rate depend on the problem to be solved, and we used another optimizer, the Sequential Parameter Optimization Toolbox (SPOT) [53], to optimize them.”

The paper can be downloaded from


[53] T. Bartz-Beielstein and M. Zaefferer, “A gentle introduction to sequential parameter optimization,” FH Köln, Tech. Rep., 2012. [Online]. Available: