Evolutionary dataset optimisation: learning algorithm quality through evolution

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:	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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