  Many real world, complex phenomena have underlying structures of evolving
networks where nodes and links are added and removed over time. A central
scientific challenge is the description and explanation of network dynamics,
with a key test being the prediction of short and long term changes. For the
problem of short-term link prediction, existing methods attempt to determine
neighborhood metrics that correlate with the appearance of a link in the next
observation period. Recent work has suggested that the incorporation of
topological features and node attributes can improve link prediction. We
provide an approach to predicting future links by applying the Covariance
Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are
used in a linear combination of sixteen neighborhood and node similarity
indices. We examine a large dynamic social network with over $10^6$ nodes
(Twitter reciprocal reply networks), both as a test of our general method and
as a problem of scientific interest in itself. Our method exhibits fast
convergence and high levels of precision for the top twenty predicted links.
Based on our findings, we suggest possible factors which may be driving the
evolution of Twitter reciprocal reply networks.
