Wilde, Henry, Knight, Vincent  ORCID: https://orcid.org/0000-0002-4245-0638 and Gillard, Jonathan ORCID: https://orcid.org/0000-0001-9166-298X
2020.
Evolutionary dataset optimisation: learning algorithm quality through evolution.
Applied Intelligence
50
, pp. 1172-1191.
10.1007/s10489-019-01592-4
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Abstract
In this paper we propose a new method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark data sets based on some fixed metrics. The algorithm(s) with the smallest value of this metric are chosen to be the `best performing'. We offer a new approach to flip this paradigm. We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs well. These data sets can be studied to learn as to what attributes lead to a particular progress of a given algorithm. Following a detailed description of the algorithm as well as a brief description of an open source implementation, a number of numeric experiments are presented to show the performance of the method which we call Evolutionary Dataset Optimisation.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Mathematics |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Publisher: | Springer |
| ISSN: | 0924-669X |
| Date of First Compliant Deposit: | 1 November 2019 |
| Date of Acceptance: | 31 October 2019 |
| Last Modified: | 24 Nov 2024 04:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/126456 |
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