论文标题
使用优胜胜和替代健康和福祉推荐系统的替代
Using coevolution and substitution of the fittest for health and well-being recommender systems
论文作者
论文摘要
这项研究探索了优胜胜（SF）的替代，该技术旨在抵消两人竞争性的协同进化遗传算法中脱离接触的问题。 SF是无关的，不需要校准。我们首先对SF保持参与度的能力并在最小玩具域中发现最佳解决方案的能力进行了受控的比较评估。实验结果表明，SF比文献中的其他技术更好地保持参与度。然后，我们解决了对健康和福祉不断发展建议的更复杂的现实世界中的问题。我们介绍了Evorecsys的共同进化扩展，这是先前发表的进化推荐系统。我们证明，与文献中的其他技术相比，SF能够更好地保持参与度，并且使用SF的建议比Evorecsys更高的质量和更多样化。
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.
