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

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


Zusammenfassung
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.

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



Publikationstyp
Forschungsartikel in Sammelband (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2016

Konferenz
Learning and Intelligent Optimization, 10th International Conference

Konferenzort
Ischia

Buchtitel
LION 2016: Learning and Intelligent Optimization

Herausgeber
Joaquin Vanschooren et al.

Erste Seite
32

Letzte Seite
47

Band
10079

Reihe
LNTCS

Verlag
Springer International Publishing

Ort
Cham

Sprache
Englisch

DOI