定性指标偏好感知进化优化及在个性化搜索中的应用

项目名称： 定性指标偏好感知进化优化及在个性化搜索中的应用

项目编号： No.61473298

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 其他

项目作者： 孙晓燕

作者单位： 中国矿业大学

项目金额： 83万元

中文摘要： 定性指标优化问题没有明确定义的目标函数，该问题普遍存在，难以求解。尽管采用交互式进化优化方法可以解决该问题，但是，需要用户根据偏好直接参与候选解性能的评价，使得评价负担急剧增加，导致用户疲劳，从而限制了该方法在复杂定性指标优化问题中的应用。本项目研究基于人-机交互行为间接感知用户偏好的定性指标进化优化理论，并应用于电商个性化搜索中。通过研究，拟揭示用户人-机交互行为与候选解性能评价之间的内在联系，建立可定量计算的用户偏好感知模型，设计基于感知模型评价候选解的高性能进化优化方法，并用于解决电商个性化快速搜索问题。本项目是自动化、计算机、数学，以及管理等学科有机交叉、新颖且富有挑战性性的研究方向，有非常明确的行业需求；产生的成果能够丰富进化优化理论，提高复杂定性指标优化问题求解方法的性能，因此，具有重要的理论意义和实际应用价值。

中文关键词： 定性指标；进化算法；偏好感知；交互；个性化搜索

英文摘要： The qualitative optimization problems are universal but difficult to be solved due to the lack of explicitly defined indicators. The interactive evolutionary algorithm (IEA) is a good choice for such problems, however, it involves a user to directly assess those candidate solutions, which evidently brings large evaluation burden and causes user fatigue. Therefore, the existing IEAs are greatly hindered in solving complicated qualitative problems. To deal with these critical issues, based on some simple user-computer interactions, this project proposes a novel evolutionary algorithm framework with induced-assessment for optimizing qualitative problems and expects to obtain the following achievements: (1) revealing the relationships between the interactions and the assessments on the candidate solutions;(2)building the computable preference model of the user to serve as a fitness function based on the relationships; (3)developing a novel evolutionary optimization framework by articulating the preference model to assess the candidate solutions and enhance the optimization performance;(4)applying the proposed theory to a specific E-commerce system to achieve personalized and fast search. The project, at the cross edge of such disciplines as automation, computer, mathematics and management, is novel and challenging, and is urgently demanded by real applications. The achievements of this project can offer a powerful and new framework for solving the qualitative problems, enrich the theory of evolutionary optimization. Therefore, the project is of considerable significance in theory and application.

英文关键词： qualitative indicator;evolutionary algorithm;induced preference;interaction;personalized search