Before revision:  QGA is similar to covariance matrix adaptation and natural evolutionary strategies in optimization, with similar performance, although QGA requires tuning of its single hyperparameter. QGA is extremely simple in implementation with no matrix inversion or factorization, does not require storing a covariance matrix, is trivial to parallelize, and may form the basis of more robust algorithms .
After revision:  QGA is similar to covariance matrix adaptation and natural evolutionary strategies in optimization, and has similar performance. The algorithm is extremely simple in implementation with no matrix inversion or factorization, does not require storing a covariance matrix, is trivial to parallelize, and may form the basis of more robust algorithms .