text: This paper proposes an evolutionary algorithm for solving the variational E step in expectation-maximization algorithm for probabilistic models with binary latent variables.  This is done by (i) considering the bit-vectors of the latent states as genomes of individuals, and by (ii) defining the fitness of the individuals as the log joint distribution of the parameters and the latent space. Pros:The paper is well written and the methodology presented is largely clear. Cons:While the reviewer is essentially fine with the idea of the method,  the reviewer is much less convinced of the empirical study.  There is no comparison with other methods such as Monte carlo sampling. It is not clear how computationally Evolutionary EM performs comparing to Variational EM algorithm  and there is neither experimental results nor analysis for the computational complexity of the proposed model. The datasets used in the experiments are quite old.  The reviewer is concerned that these datasets may not be representative of real problems. The applicability of the method is quite limited.  The proposed model is only applicable for the probabilistic models with binary latent variables,  hence it cannot be applied to more realistic complex model with real-valued latent variables.