Towards transferring algorithm configurations across problems

Alberto Franzin, Thomas Stützle

Keywords: metaheuristics, transfer learning, algorithm configuration

TL;DR: We show a proof of concept of transfer learning of configurations for optimization algorithms across different problems.

Abstract:

Automatic approaches for algorithm configuration and design
have received significant attention in the last years, thanks to
both the potential to obtain high performing algorithms, and
the ease for algorithm designers and practitioners.
One limitation of current methods is the need to repeat the task
for every new scenario encountered. We show how the observation of
problem-independent features of the solution landscape can enable
the use of past experiments to infer good configurations for unseen
scenarios, both in case of new instances and new problems.
As a proof of concept, we report preliminary experiments obtained when
configuring a metaheuristic with two parameters.