Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

Ke Li, Zilin Xiang, Kay Chen Tan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1988-1995
Number of pages8
ISBN (Electronic)9781728121536
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
Country/TerritoryNew Zealand
CityWellington
Period10/06/1913/06/19

Keywords

  • differential evolution
  • Empirical performance modelling
  • landscape analysis
  • parameter configuration

ASJC Scopus subject areas

  • Computational Mathematics
  • Modelling and Simulation

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