  Efficient Natural Evolution Strategies (eNES) is a novel alternative to
conventional evolutionary algorithms, using the natural gradient to adapt the
mutation distribution. Unlike previous methods based on natural gradients, eNES
uses a fast algorithm to calculate the inverse of the exact Fisher information
matrix, thus increasing both robustness and performance of its evolution
gradient estimation, even in higher dimensions. Additional novel aspects of
eNES include optimal fitness baselines and importance mixing (a procedure for
updating the population with very few fitness evaluations). The algorithm
yields competitive results on both unimodal and multimodal benchmarks.
