A Survey of Automatic Parameter Tuning Methods for Metaheuristics

Changwu HUANG, Yuanxiang LI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

213 Citations (Scopus)


Parameter tuning, that is, to find appropriate parameter settings (or configurations) of algorithms so that their performance is optimized, is an important task in the development and application of metaheuristics. Automating this task, i.e., developing algorithmic procedure to address parameter tuning task, is highly desired and has attracted significant attention from the researchers and practitioners. During last two decades, many automatic parameter tuning approaches have been proposed. This paper presents a comprehensive survey of automatic parameter tuning methods for metaheuristics. A new classification (or taxonomy) of automatic parameter tuning methods is introduced according to the structure of tuning methods. The existing automatic parameter tuning approaches are consequently classified into three categories: 1) simple generate-evaluate methods; 2) iterative generate-evaluate methods; and 3) high-level generate-evaluate methods. Then, these three categories of tuning methods are reviewed in sequence. In addition to the description of each tuning method, its main strengths and weaknesses are discussed, which is helpful for new researchers or practitioners to select appropriate tuning methods to use. Furthermore, some challenges and directions of this field are pointed out for further research. © 1997-2012 IEEE.
Original languageEnglish
Article number8733017
Pages (from-to)201-216
Number of pages16
JournalIEEE Transactions on Evolutionary Computation
Issue number2
Early online date7 Jun 2019
Publication statusPublished - Apr 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFC0804002, in part by the Engineering and Physical Sciences Research Council under Grant EP/J017515/1 and Grant EP/P005578/1, in part by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams under Grant 2017ZT07X386, in part by the Shenzhen Peacock Plan under Grant KQTD2016112514355531, in part by the Science and Technology Innovation Committee Foundation of Shenzhen under Grant ZDSYS201703031748284 and Grant JCYJ20180504165652917, and in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008.


  • Automatic parameter tuning
  • metaheuristics
  • parameter setting
  • parameter tuning


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