Evolutionary algorithms are effective methods for solving optimization problems that do not require any special assumptions to find optimal solutions. Optimization based on teaching and learning has been parallelized using CUDA parallelization technology. The results show that the proposed parallel version of the algorithm is fast and efficient in teaching and learning phases
