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The dynamic stochastic block model (SBM) is used to analyze longitudinal network data when multiple snapshots are observed over time. The variational expectation-maximization (VEM) algorithm is commonly employed for maximum likelihood inference to allocate nodes to groups dynamically. To address the problem of multiple local maxima, which may arise in this context, we propose modifying the VEM according to an evolutionary algorithm to explore the whole parameter space. Simulated data on dynamic networks and an application illustrate the proposal.