Sample size estimation for power and accuracy in the experimental comparison of algorithms
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 34748100
- Type
- D - Journal article
- DOI
-
10.1007/s10732-018-9396-7
- Title of journal
- Journal of Heuristics
- Article number
- -
- First page
- 305
- Volume
- 25
- Issue
- 2
- ISSN
- 1381-1231
- Open access status
- Deposit exception
- Month of publication
- October
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a mathematical treatment of sample size and statistical power calculations for experimental research in optimisation, laying the foundations for the development of statistically sound comparisons of algorithms. This research has generated an open-source R package (https://cran.r-project.org/package=CAISEr), which according to download statistics from CRAN has been downloaded around 16,000 times since it was first made public. The main results were presented as an invited talk at the Computational Intelligence for Massive Optimisation Workshop 2019 (Lille, France) and already led to a follow-up paper (J. Heuristics v.26, pp. 851-883 (2020))
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
