中文摘要：

融合了用户认知和智能评价的交互式遗传算法（Interactivegeneticalgorithm，IGA）是解决一类定性性能指标优化问题的有效方法，但是，评价不确定性和易疲劳性极大地限制了该算法解决实际问题的能力．基于用户己评价信息，采用合适的机器学习方法，构建用户认知代理模型是解决上述问题的常用方法之一．但是，现有研究成果均没有考虑用户评价不确定性对学习样本、代理模型的影响，以及模型拟合不确定性对基于适应值的进化操作有效性的影响．针对上述问题，本文提出基于加权多输出高斯过程（Gaussianprocess，GP）代理模型的交互式遗传算法．首先，在区间适应值评价模式下，提取学习样本的噪声特性，以确定相应学习样本对代理模型的影响度权重系数，构建两输出高斯过程代理模型；然后，利用代理模型提供的预测值及预测置信水平，给出一种新的个体适应值估计方法和个体选择方法；基于模型预测信息，实现模型更新管理．将所提算法分别应用于含噪函数和服装设计问题中，所得结果表明本文算法可更好地拟合和跟踪用户认知，减小对进化搜索的误导，更快找到用户满意解．

英文摘要：

An interactive genetic algorithm （ICA）, combining a user＇s intelligent evaluations with traditional genetic operators, is developed to optimize problems with aesthetic indicators. However, the evaluation uncertainties and burden greatly restrict the applications of ICA in complicated situations. Surrogate models constructed with appropriate machine learning methods have been successfully used to alleviate the user evaluation burden of IGAs. However, the uncertainties resulted from the userts evaluations and model＇s approximation are not taken into account in the existing research. To tackle such problems, a weighted multi-output Gaussian process （GP） is proposed to build a surrogate model to improve the performance of IGA. First, the evaluation noise is defined when an individual＇s fitness is represented as an interval. With the evaluation noise, the contribution of a training sample to construct the surrogate model is calculated, and used to train a GP with two outputs to approximate the upper and lower values of the interval fitness. A novel fitness approximation method is proposed by combining the predicted value with its associated predictive confidence. Based on the predictive confidence, the surrogate model is well managed during the evolution. The proposed algorithm is used to optimize a benchmark function and a real-world fashion design case to experimentally demonstrate that the surrogate model outperforms others in prediction fitting and tracking user＇s evaluations, and is beneficial to less misleading the search and faster finding the optima.