IEEE Trans Cybern. 2013 Apr;43(2):685-98. doi: 10.1109/TSMCB.2012.2214382. Epub 2013 Mar 7.
Surrogate-assisted interactive genetic algorithms (IGAs) are found to be very effective in reducing human fatigue. Different from models used in most surrogate-assisted evolutionary algorithms, surrogates in IGA must be able to handle the inherent uncertainties in fitness assignment by human users, where, e.g., interval-based fitness values are assigned to individuals. This poses another challenge to using surrogates for fitness approximation in evolutionary optimization, in addition to the lack of training data. In this paper, a new surrogate-assisted IGA has been proposed, where the uncertainty in subjective fitness evaluations is exploited both in training the surrogates and in managing surrogates. To enhance the approximation accuracy of the surrogates, an improved cotraining algorithm for semisupervised learning has been suggested, where the uncertainty in interval-based fitness values is taken into account in training and weighting the two cotrained models. Moreover, uncertainty in the interval-based fitness values is also considered in model management so that not only the best individuals but also the most uncertain individuals will be chosen to be re-evaluated by the human user. The effectiveness of the proposed algorithm is verified on two test problems as well as in fashion design, a typical application of IGA. Our results indicate that the new surrogate-assisted IGA can effectively alleviate user fatigue and is more likely to find acceptable solutions in solving complex design problems.
代理辅助交互式遗传算法 (IGA) 被发现非常有效地减轻人类疲劳。与大多数代理辅助进化算法中使用的模型不同，IGA 中的代理必须能够处理人类用户分配适应度时固有的不确定性，例如，为个体分配基于区间的适应度值。除了缺乏训练数据之外，这给进化优化中使用代理进行适应度逼近带来了另一个挑战。在本文中，提出了一种新的代理辅助 IGA，其中在训练代理和管理代理时都利用了主观适应度评估中的不确定性。为了提高代理的逼近精度，提出了一种用于半监督学习的改进协同训练算法，其中考虑了基于区间的适应度值的不确定性，以训练和加权两个协同训练的模型。此外，还考虑了基于区间的适应度值的不确定性，以便不仅选择最佳个体，而且还选择最不确定的个体由人类用户重新评估。所提出的算法在两个测试问题以及时装设计（IGA 的典型应用）中得到了验证。我们的结果表明，新的代理辅助 IGA 可以有效地减轻用户疲劳，并更有可能在解决复杂设计问题时找到可接受的解决方案。
