MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework

Blot A, Hoos H, Jourdan L, Marmion M, Trautmann H


Abstract
Automated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator.

Keywords
Algorithm Configuration, Parameter tuning, Multi-objective optimisation, Local search algorithms



Publication type
Research article in proceedings (conference)

Peer reviewed
Yes

Publication status
Published

Year
2016

Conference
Learning and Intelligent Optimization, 10th International Conference

Venue
Ischia

Book title
LION 2016: Learning and Intelligent Optimization

Editor
Joaquin Vanschooren et al.

Start page
32

End page
47

Volume
10079

Title of series
LNTCS

Publisher
Springer International Publishing

Place
Cham

Language
English

DOI