Black Box Optimization with Data Analysis

by Kevin Kofler

This page presents an algorithm solving black box global optimization problems using mainly methods from data analysis and an implementation of this algorithm. As documentation, you may download a copy of the thesis.

The main goal of my thesis was to cross the barrier between the fields of optimization and data analysis by applying methods from data analysis to optimization problems. In particular, I applied data analysis techniques to obtain information about black box functions, i.e. functions for which we do not know an algebraic expression.

I presented both an algorithm and a reference implementation to solve optimization problems where:

using methods from data analysis: The algorithm is a so-called incomplete global optimizer, i.e. it attempts to find a global solution for the optimization problem, but is unable to guarantee globality. In fact, it cannot even guarantee always finding a local optimum, due to the lack of gradients and any sort of global information. Despite this lack of guarantees, the algorithm performs well in practice.

The implementation is licensed under the GNU General Public License, version 3 or later, with special exceptions allowing to link with the third-party optimizers used.

You can cite the thesis as follows:
Kevin Kofler: Black Box Optimization with Data Analysis. Diploma thesis. Universit├Ąt Wien, 2007.
Please include a citation like the above if you use my work in any research paper.