A review of no free lunch theorems, and their implications for metaheuristic optimisation

Thomas Joyce, J. Michael Herrmann

Research output: Chapter in Book/Report/Conference proceedingChapter


The No Free Lunch Theorem states that, averaged over all optimisation problems, all non-resampling optimisation algorithms perform equally well. In order to explain the relevance of these theorems for metaheuristic optimisation, we present a detailed discussion on the No Free Lunch Theorem, and various extensions including some which have not appeared in the literature so far. We then show that understanding the No Free Lunch theorems brings us to a position where we can ask about the specific dynamics of an optimisation algorithm, and how those dynamics relate to the properties of optimisation problems.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
Number of pages25
ISBN (Electronic)978-3-319-67669-2
ISBN (Print)978-3-319-67668-5
Publication statusE-pub ahead of print - 10 Oct 2017

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860949X


  • Metaheuristics
  • No free lunch (NFL)
  • Optimisation
  • Search

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