  We introduce a novel distributed derivative-free optimization framework that
is resilient to stragglers. The proposed method employs coded search directions
at which the objective function is evaluated, and a decoding step to find the
next iterate. Our framework can be seen as an extension of evolution strategies
and structured exploration methods where structured search directions were
utilized. As an application, we consider black-box adversarial attacks on deep
convolutional neural networks. Our numerical experiments demonstrate a
significant improvement in the computation times.
