  The Influence Maximization (IM) problem is a well-known NP-hard combinatorial
problem over graphs whose goal is to find the set of nodes in a network that
spreads influence at most. Among the various methods for solving the IM
problem, evolutionary algorithms (EAs) have been shown to be particularly
effective. While the literature on the topic is particularly ample, only a few
attempts have been made at solving the IM problem over higher-order networks,
namely extensions of standard graphs that can capture interactions that involve
more than two nodes. Hypergraphs are a valuable tool for modeling complex
interaction networks in various domains; however, they require rethinking of
several graph-based problems, including IM. In this work, we propose a
multi-objective EA for the IM problem over hypergraphs that leverages smart
initialization and hypergraph-aware mutation. While the existing methods rely
on greedy or heuristic methods, to our best knowledge this is the first attempt
at applying EAs to this problem. Our results over nine real-world datasets and
three propagation models, compared with five baseline algorithms, reveal that
our method achieves in most cases state-of-the-art results in terms of
hypervolume and solution diversity.
