  The aim of global optimization is to find the global optimum of arbitrary
classes of functions, possibly highly multimodal ones. In this paper we focus
on the subproblem of global optimization for differentiable functions and we
propose an Evolutionary Search-inspired solution where we model point search
distributions via Generative Neural Networks. This approach enables us to model
diverse and complex search distributions based on which we can efficiently
explore complicated objective landscapes. In our experiments we show the
practical superiority of our algorithm versus classical Evolutionary Search and
gradient-based solutions on a benchmark set of multimodal functions, and
demonstrate how it can be used to accelerate Bayesian Optimization with
Gaussian Processes.
