Computer Science > Neural and Evolutionary Computing
[Submitted on 1 Nov 2022 (v1), last revised 6 Dec 2022 (this version, v2)]
Title:Using coevolution and substitution of the fittest for health and well-being recommender systems
View PDFAbstract:This research explores substitution of the fittest (SF), a technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. SF is domain-independent and requires no calibration. We first perform a controlled comparative evaluation of SF's ability to maintain engagement and discover optimal solutions in a minimal toy domain. Experimental results demonstrate that SF is able to maintain engagement better than other techniques in the literature. We then address the more complex real-world problem of evolving recommendations for health and well-being. We introduce a coevolutionary extension of EvoRecSys, a previously published evolutionary recommender system. We demonstrate that SF is able to maintain engagement better than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.
Submission history
From: John Cartlidge [view email][v1] Tue, 1 Nov 2022 12:16:11 UTC (3,813 KB)
[v2] Tue, 6 Dec 2022 18:08:19 UTC (5,497 KB)
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