  Influence maximization (IM) aims to select a small number of nodes that are
able to maximize their influence in a network and covers a wide range of
applications. Despite numerous attempts to provide effective solutions in
ordinary networks, higher-order interactions between entities in various
real-world systems are not usually taken into account. In this paper, we
propose a versatile meta-heuristic approach, hyper genetic algorithm (HGA), to
tackle the IM problem in hypergraphs, which is based on the concept of genetic
evolution. Systematic validations in synthetic and empirical hypergraphs under
both simple and complex contagion models indicate that HGA achieves universal
and plausible performance compared to baseline methods. We explore the cause of
the excellent performance of HGA through ablation studies and correlation
analysis. The findings show that the solution of HGA is distinct from that of
other prior methods. Moreover, a closer look at the local topological features
of the seed nodes acquired by different algorithms reveals that the selection
of seed nodes cannot be based on a single topological characteristic, but
should involve a combination of multiple topological features to address the IM
problem.
