An experimental investigation of model-based parameter optimisation: SPO and beyond

  • Authors:

  • Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown;Kevin P. Murphy

  • Affiliations:

  • The University of British Columbia, Vancouver, BC, Canada;The University of British Columbia, Vancouver, BC, Canada;The University of British Columbia, Vancouver, BC, Canada;The University of British Columbia, Vancouver, BC, Canada

  • Venue:
  • Proceedings of the 11th Annual conference on Genetic and evolutionary computation
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This work experimentally investigates model-based approaches for optimising the performance of parameterised randomised algorithms. We restrict our attention to procedures based on Gaussian process models, the most widely-studied family of models for this problem. We evaluated two approaches from the literature, and found that sequential parameter optimisation (SPO) [4] offered the most robust performance. We then investigated key design decisions within the SPO paradigm, characterising the performance consequences of each. Based on these findings, we propose a new version of SPO, dubbed SPO+, which extends SPO with a novel intensification procedure and log-transformed response values. Finally, in a domain for which performance results for other (model-free) parameter optimisation approaches are available, we demonstrate that SPO+ achieves state-of-the-art performance.