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Evolutionary dataset optimisation: learning algorithm quality through evolution

Wilde, Henry, Knight, Vincent and Gillard, Jonathan 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: 12 Apr 2020 17:51
URI: http://orca.cf.ac.uk/id/eprint/126456

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