工业设计关注用户感性意象，而众多感性评价指标与设计要素之间存在复杂的映射关系。基于工业设计问题的网络特性实证：以设计要素和子目标为两类节点建立二分图；借助基因调控网络(GRN)理论发现节点之间的边，建立双层网络模型；借助复杂网络技术进行知识发现，得到关键要素、要素群落、问题简化、子目标聚类等知识，并利用知识辅助设计师、辅助CAD优化流程：1)辅助设计师识别设计要素与目标之间隐含的因果关系模式，实现对设计目标的精确控制；2)研制基于交互式遗传算法(IGA)的优化机制，利用GRN动力学模型构建目标函数，利用GRN拓扑结构模型重构产品基因编码，使之携带网络结构信息，提高进化效率。 课题为工业设计引入复杂网络理论，把产品基因的研究从目前的"自然选择"提升到"基因手术"模式；为设计师提供直观辅助，改变CAD技术谋求"取代"而不是"辅助"设计师的不足；实现"设计师-用户-CAD"的协同创新机制。

Industrial design pays much concerns on user's perceptual images, in which complex mapping relations exist between the perceptual evaluation goals and the design elements. Based on real evidence of industrial design problem's network features: bipartite graph is established taking design elements and subgoals as the two tpyes of nodes; a double-layer network model is built with the edges connected via GRN theory; complex network theories are applied to discover design knowledges, including key elements, element clique, problem simplification, subgoals clustering, etc., which are used to aid designers and CAD optimization applications. 1) To help the designers to recognize the hidden connections between design elements and subgoals, and to realize the precise controlling of the design; 2) to establish the IGA-based optimization mechanisms, in which GRN dynamic models are used to construct target function, and the GRN topologic models are used to reconstruct the product gene code to make it able to convey network structure information and improve the evolving efficiency. The project will introduce complex network theories into industrial design, which will promote product gene studies' natural selection mode to genetic operation mode, and will provide comprehensible aids to designers, i.e. to aid instead of replace them with CAD tools. The collaborative innovation mechanism of designers, users and CAD will also be realized.