A Generation Gap Model for a Human-Based Evolutionary Algorithm Using a Tag Cloud
A Generation Gap Model for a Human-Based Evolutionary Algorithm Using a Tag Cloud
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使用标签云的基于人的进化算法的代沟模型
DOI：
10.1109/scis-isis.2016.0191
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
K. Ohnishi
中科院分区：
文献类型：
--
作者：
M. Azumaya;K. Ohnishi
我们以前提出了一个基于人类的进化算法（基于人类的EA），使用标签云，在本文中，我们提出了一个代沟模型的基于人类的EA。在前一个模型中，人们创建候选解决方案，创建的候选解决方案显示为标记云中的标记。然后，人们评估候选标签，那些具有最大适应性的标签使用更大的字体显示。然而，在以前的模型中，标签云中可以显示的标签数量是有限制的，达到这个数量后，如果创建了新的标签，它们将不会被包括在内。因此，我们提出了一个代沟模型来解决这个问题，允许每个后续的一代，包括所有的标签从上一代的健身水平大于一个给定的阈值。标记云中的其余部分将被队列中的标记替换，这些标记以前创建过，从未显示过。我们使用基于人类的EA与代沟模型进行了一项实验，结果表明，几乎所有的参与者都认为我们的系统创建的标签比他们自己创建的标签更好。
We previously proposed a human-based evolutionary algorithm (human-based EA) that used a tag cloud, and in this paper, we propose a generation gap model for the human-based EA. In the previous model, people created candidate solutions, and the created candidate solutions were displayed as tags in a tag cloud. People then evaluated the candidate tags, and those with the greatest fitness were displayed using a larger font size. However, in the previous model, there was a limit on the number of tags that could be displayed in the tag cloud, and after that number had been reached, if new tags were created, they would not be included. Therefore, we propose a generation gap model for solving this problem by allowing each subsequent generation to include all of the tags from the previous generation that have a fitness level that is greater than a given threshold. The remainder in the tag cloud are replaced by tags in a queue for storage that were created before and have never displayed. We carried out an experiment using the human-based EA with the generation gap model, and the results showed that almost all participants believed the tags created by our system were better than those that they would have created by themselves.