Abstract / Description of output
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 language | English |
---|---|
Title of host publication | Studies in Computational Intelligence |
Publisher | Springer |
Pages | 27-51 |
Number of pages | 25 |
Volume | 744 |
ISBN (Electronic) | 978-3-319-67669-2 |
ISBN (Print) | 978-3-319-67668-5 |
DOIs | |
Publication status | E-pub ahead of print - 10 Oct 2017 |
Publication series
Name | Studies in Computational Intelligence |
---|---|
Volume | 744 |
ISSN (Print) | 1860949X |
Keywords / Materials (for Non-textual outputs)
- Metaheuristics
- No free lunch (NFL)
- Optimisation
- Search
Fingerprint
Dive into the research topics of 'A review of no free lunch theorems, and their implications for metaheuristic optimisation'. Together they form a unique fingerprint.Profiles
-
Michael Herrmann
- School of Informatics - Lectureship in Robotics
- Institute of Perception, Action and Behaviour
- Edinburgh Neuroscience
- Data Science and Artificial Intelligence
Person: Academic: Research Active