Meta-algorithmics, the field of leveraging machine learning to use algorithms more efficiently, has achieved impressive performance improvements in many areas of AI. It treats the algorithms to improve on as black boxes – nothing is known about their inner workings. This allows meta-algorithmic techniques to be deployed in many applications, but leaves potential performance improvements untapped by ignoring information that the algorithms could provide. In this paper, we open the black box without sacrificing the universal applicability of meta-algorithmic techniques by automatically analyzing the source code of the algorithms under consideration and show how to use it to improve algorithm selection performance. We demonstrate improvements of up to 82% on the standard ASlib benchmark library.
Published Date: 2020-06-02
Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print) ISBN 978-1-57735-835-0 (10 issue set)
Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2020, Association for the Advancement of Artificial Intelligence All Rights Reserved