Estimation of Distribution using Population Queue based Variational Autoencoders

We present a new Estimation of Distribution algorithms (EDA) based on two novel Variational Autoencoders generative model building algorithms. The first method, Variational Autoencoder with Population Queue (VAE-EDA-Q), employs a queue of historical populations, which is updated at each iteration of EDA in order to smooth the data generation process. The second method uses Adaptive Variance Scaling (AVS) with VAE-EDA-Q to dynamically update the variance at which the probabilistic model is sampled. The results obtained prove our methods to be significantly more computationally efficient than state-of-the-art algorithms and perform significantly less number of fitness evaluations when tested on benchmark problems such as Trap-k and NK Landscapes. Moreover, we report results of applying our approach successfully to highly complex problems such as Trap 11, Trap 13, and NK Landscapes with neighborhood size K = 8 and K = 10.