Evolutionary dataset optimisation: learning algorithm quality through evolution

Volume: 50, Issue: 4, Pages: 1172 - 1191
Published: Dec 27, 2019
Abstract
In this paper we propose a novel method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark datasets 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...
Paper Details
Title
Evolutionary dataset optimisation: learning algorithm quality through evolution
Published Date
Dec 27, 2019
Volume
50
Issue
4
Pages
1172 - 1191
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