  The content ranking problem in a social news website, is typically a function
that maximizes a scalar metric of interest like dwell-time. However, like in
most real-world applications we are interested in more than one metric---for
instance simultaneously maximizing click-through rate, monetization metrics,
dwell-time---and also satisfy the traffic requirements promised to different
publishers. All this needs to be done on online data and under the settings
where the objective function and the constraints can dynamically change; this
could happen if for instance new publishers are added, some contracts are
adjusted, or if some contracts are over.
  In this paper, we formulate this problem as a constrained, dynamic,
multi-objective optimization problem. We propose a novel framework that extends
a successful genetic optimization algorithm, NSGA-II, to solve this online,
data-driven problem. We design the modules of NSGA-II to suit our problem. We
evaluate optimization performance using Hypervolume and introduce a confidence
interval metric for assessing the practicality of a solution. We demonstrate
the application of this framework on a real-world Article Ranking problem. We
observe that we make considerable improvements in both time and performance
over a brute-force baseline technique that is currently in production.
