Incorporating multiple elements of evolution strategies, a first-order variable metric method for
unconstrained optimization that is invariant to strictly increasing function value transformations is
proposed. The algorithm’s performance is evaluated relative to that of a quasi-Newton algorithm
using a large set of test problems.
