  Complex phenomena are generally modeled with sophisticated simulators that,
depending on their accuracy, can be very demanding in terms of computational
resources and simulation time. Their time-consuming nature, together with a
typically vast parameter space to be explored, make simulation-based
optimization often infeasible. In this work, we present a method that enables
the optimization of complex systems through Machine Learning (ML) techniques.
We show how well-known learning algorithms are able to reliably emulate a
complex simulator with a modest dataset obtained from it. The trained emulator
is then able to yield values close to the simulated ones in virtually no time.
Therefore, it is possible to perform a global numerical optimization over the
vast multi-dimensional parameter space, in a fraction of the time that would be
required by a simple brute-force search. As a testbed for the proposed
methodology, we used a network simulator for next-generation mmWave cellular
systems. After simulating several antenna configurations and collecting the
resulting network-level statistics, we feed it into our framework. Results show
that, even with few data points, extrapolating a continuous model makes it
possible to estimate the global optimum configuration almost instantaneously.
The very same tool can then be used to achieve any further optimization goal on
the same input parameters in negligible time.
