  Computing maximum weight independent sets in graphs is an important NP-hard
optimization problem. The problem is particularly difficult to solve in large
graphs for which data reduction techniques do not work well. To be more
precise, state-of-the-art branch-and-reduce algorithms can solve many
large-scale graphs if reductions are applicable. Otherwise, their performance
quickly degrades due to branching requiring exponential time. In this paper, we
develop an advanced memetic algorithm to tackle the problem, which incorporates
recent data reduction techniques to compute near-optimal weighted independent
sets in huge sparse networks. More precisely, we use a memetic approach to
recursively choose vertices that are likely to be in a large-weight independent
set. We include these vertices into the solution, and further reduce the graph.
We show that identifying and removing vertices likely to be in large-weight
independent sets opens up the reduction space and speeds up the computation of
large-weight independent sets remarkably. Our experimental evaluation indicates
that we are able to outperform state-of-the-art algorithms. For example, our
two algorithm configurations compute the best results among all competing
algorithms for 205 out of 207 instances. Thus can be seen as a useful tool when
large-weight independent sets need to be computed in~practice.
