This paper concerns efficient parameters tuning (meta-optimization) of a state-of-the-art metaheuristic, Quantum-Inspired Genetic
Algorithm (QIGA), in a GPU-based massively parallel computing environment (NVidia CUDATMtechnology). A novel approach to parallel
implementation of the algorithm has been presented. In a block of threads, each thread transforms a separate quantum individual or different
quantum gene; In each block, a separate experiment with different population is conducted. The computations have been distributed to
eight GPU devices, and over 400x speedup has been gained in comparison to Intel Core i7 2.93GHz CPU. This approach allows efficient
meta-optimization of the algorithm parameters. Two criteria for the meta-optimization of the rotation angles in quantum genes state space
have been considered. Performance comparison has been performed on combinatorial optimization (knapsack problem), and it has been
presented that the tuned algorithm is superior to Simple Genetic Algorithm and to original QIGA algorithm.