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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation (CEC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1988-1995
ISBN (Electronic)9781728121536
ISBN (Print)9781728121543
Publication statusPublished - Jun 2019

Publication series

NameIEEE Congress on Evolutionary Computation, CEC - Proceedings

Conference

Title2019 IEEE Congress on Evolutionary Computation, CEC 2019
PlaceNew Zealand
CityWellington
Period10 - 13 June 2019

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.

Research Area(s)

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

Citation Format(s)

Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution. / Li, Ke; Xiang, Zilin; Tan, Kay Chen.

2019 IEEE Congress on Evolutionary Computation (CEC). Institute of Electrical and Electronics Engineers Inc., 2019. p. 1988-1995 8789984 (IEEE Congress on Evolutionary Computation, CEC - Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review