Pei Yan, Zhao Qiangfu, Liu Yong
The University of Aizu, Tsuruga, Ikki-machi, Aizuwakamatsu, Fukushima 965-8580, Japan.
ScientificWorldJournal. 2015;2015:185860. doi: 10.1155/2015/185860. Epub 2015 Mar 23.
A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). The fitness landscape of human subjective evaluation in IEC is very difficult and impossible to model, even with a hypothesis of what its definition might be. In this paper, we propose a method to establish a human model in projected high dimensional search space by kernel classification for enhancing IEC search. Because bivalent logic is a simplest perceptual paradigm, the human model is established by considering this paradigm principle. In feature space, we design a linear classifier as a human model to obtain user preference knowledge, which cannot be supported linearly in original discrete search space. The human model is established by this method for predicting potential perceptual knowledge of human. With the human model, we design an evolution control method to enhance IEC search. From experimental evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search significantly.
在进化计算（EC）中，适应度景观呈现了个体与其繁殖成功率之间的关系。然而，原始搜索空间中的离散且近似的景观可能无法为进化计算搜索提供足够且准确的信息，尤其是在交互式进化计算（IEC）中。即使对人类主观评价在交互式进化计算中的适应度景观的定义进行假设，对其进行建模也是非常困难甚至不可能的。在本文中，我们提出了一种通过核分类在投影高维搜索空间中建立人类模型的方法，以增强交互式进化计算搜索。由于二值逻辑是最简单的感知范式，所以通过考虑该范式原理来建立人类模型。在特征空间中，我们设计一个线性分类器作为人类模型来获取用户偏好知识，而这些知识在原始离散搜索空间中无法得到线性支持。通过这种方法建立人类模型以预测人类潜在的感知知识。利用该人类模型，我们设计了一种进化控制方法来增强交互式进化计算搜索。从对一个伪交互式进化计算用户的实验评估结果来看，我们提出的模型和方法能够显著增强交互式进化计算搜索。
