Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (2): 146-151.DOI: 10.3778/j.issn.1002-8331.1810-0089

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Interactive Genetic Algorithm Based on BP Neural Network and User Cognitive Surrogate Model

ZHU Miaomiao, PAN Weijie, LIU Xiang, LV Jian, ZHAO Huiliang   

  1. 1.Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
    2.Guizhou Minzu University, Guiyang 550025, China
  • Online:2020-01-15 Published:2020-01-14



  1. 1.贵州大学 现代制造技术教育部重点实验室,贵阳 550025
    2.贵州民族大学,贵阳 550025

Abstract: To solve the problem of evaluation noise and user fatigue in the Interactive Genetic Algorithm(IGA), an error Back?Propagation(BP) neural network user cognitive surrogate model is proposed to improve the performance of IGA. First, because user perceptions are uncertain, a model for noises is built to form the adaptive value with individual dynamic fuzzy interval. Then user cognitive surrogate model based on BP neural network is constructed by historical evaluation information when the user’s cognition is certain and a new adaptive value estimation strategy is presented. By measuring Mean Squared Error(MSE), the management and update of the agent model are realized. This method is applied to selection system of pattern design with batik style, the results show that it can effectively optimize the quality of adaptive value with individual and reduce user fatigue.

Key words: Interactive Genetic Algorithm(IGA), error Back?Propagation(BP) neural network, surrogate model, evaluation noise, user fatigue

摘要: 针对交互式遗传算法存在用户评价噪声和审美疲劳的问题,提出一种基于误差反向传播神经网络用户认知代理模型的交互式遗传算法。通过构建用户评价噪声模型,形成进化个体动态模糊区间适应值,以刻画用户认知随机不确定性;在用户认知确定阶段历史评价信息基础上,构建误差反向传播神经网络代理模型,给出一种新的适应值估计策略;通过度量均方误差,实现代理模型的管理与更新。将所提方法应用于蜡染风格图案设计,并与其他典型算法对比。结果表明,该方法能够有效优化进化个体适应值质量、降低用户审美疲劳。

关键词: 交互式遗传算法, 误差反向传播神经网络, 代理模型, 评价噪声, 用户疲劳