Incorporating multiple elements of evolution strategies, a first-order method for unconstrained optimization that is invariant to strictly increasing function value transformations is proposed. The algorithm subjects normalized gradient vectors to a linear transformation that is adapted based on the directions of successive gradients. In computer experiments, the algorithm is found to often locate near optimal solutions to non-quadratic problems with fewer function evaluations than a quasi-Newton algorithm and a trust-region method.
Arnold et al. (Sat,) studied this question.