Polynomial-Model-Based Optimization for Blackbox Objectives
Janina Schreiber Damar Wicaksono Michael Hecht
For a wide range of applications the structure of systems like Neural
Networks or complex simulations, is unknown and approximation is costly or even
impossible. Black-box optimization seeks to find optimal (hyper-) parameters
for these systems such that a pre-defined objective function is minimized.
Polynomial-Model-Based Optimization (PMBO) is a novel blackbox optimizer that
finds the minimum by fitting a polynomial surrogate to the objective function.
  Motivated by Bayesian optimization the model is iteratively updated according
to the acquisition function Expected Improvement, thus balancing the
exploitation and exploration rate and providing an uncertainty estimate of the
model. PMBO is benchmarked against other state-of-the-art algorithms for a
given set of artificial, analytical functions. PMBO competes successfully with
those algorithms and even outperforms all of them in some cases. As the results
suggest, we believe PMBO is the pivotal choice for solving blackbox
optimization tasks occurring in a wide range of disciplines.