Hyper-parameterized dialectic search for non-linear box-constrained optimization with heterogenous variable types

Abstract

We consider the dialectic search paradigm for box-constrained, non-linear optimization with heterogeneous variable types. In particular, we devise an implementation that can handle any computable objective function, including non-linear, non-convex, non-differentiable, non-continuous, non-separable and multi-modal functions. The variable types we consider are bounded continuous and integer, as well as categorical variables with explicitly enumerated domains. Extensive experimental results show the effectiveness of the new local search solver for these types of problems.