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图学学报 ›› 2021, Vol. 42 ›› Issue (2): 332-338.DOI: 10.11996/JG.j.2095-302X.2021020332

• 工业设计 • 上一篇    下一篇

面向知识重用和智能创新的交互界面进化设计研究

  

  1. 西南交通大学建筑与设计学院,四川 成都 611756
  • 出版日期:2021-04-30 发布日期:2021-04-30
  • 基金资助:
    教育部人文社会科学研究项目(19YJA760094);四川省社会科学规划项目“四川统计发展专项课题”(SC20TJ031);四川省哲学社会科 学重点研究基地-现代设计与文化研究中心重点项目(MD20Z001);工效学会-津发优秀青年学者联合研究项目(CES-Kingfar-2019-001); 教育部人文社科青年基金项目(19YJC760054) 

Research on evolutionary design of interactive interface for knowledge reuse and intelligent innovation 

  1. School of Architecture and Design, Southwest Jiaotong University, Chengdu Sichuan 611756, China
  • Online:2021-04-30 Published:2021-04-30
  • Supported by:
    MOE Layout Foundation of Humanities and Social Sciences (19YJA760094); Sichuan Social Science Planning Fund Program: Sichuan Statistical Development Special Project (SC20TJ031); Sichuan Key Research Base of Philosophy and Social Science Key Project of Modern Design and Culture Research Center (MD20Z001); CES-Kingfar Excellent YoungScholar Joint Research Funding (CES-Kingfar-2019-001); Humanities and Social Science Youth Fund Project of Ministry of Education (19YJC760054)

摘要: 为了让交互界面脱离依赖设计师个人经验和主观认知的传统设计方式,将设计知识加以重用, 并为人工设计提供自动智能的创新启发。基于交互产品中常见的交互界面构建重用知识规则和边界,构建了交 互界面进化基因模型,并利用遗传算法,通过构建针对交互界面编码的遗传算子、重用知识约束规则库和交互 式评价等进化模块,形成了面向知识重用和自动化智能创新的交互界面进化设计方法。最后,在方法基础上, 以目前常见的 5 个交互登录界面作为初始种群,进化生成了 45 个有效的子代个体。并通过设计实践验证了方 法的有效性。结果表明,设计知识通过设计规则的方式被总结重用,并通过约束进化的方式进行了自动智能化 式的辅助设计,提升了设计知识重用及计算机辅助智能创新水平。

关键词: 知识重用, 智能创新, 进化, 交互界面, 遗传算法

Abstract: In order to break away from the traditional design method depending on the designer’s personal experience and subjective cognition, to reuse design knowledge, and to provide innovative inspiration for artificial design with automatic intelligence, the evolutionary gene model of interactive interface was constructed based on the reuse knowledge rules and boundaries summarized in the process of interactive interface design. Then, the genetic algorithm was employed to construct the evolutionary modules of the interactive interface coding, such as genetic operators, knowledge reuse constraint rules base, and interactive semantic evaluation, forming an interactive interface evolutionary design method for knowledge reuse and automation intelligent innovation. Finally, on the basis of the method, forty-five effective offspring individuals were generated from the ten generations of evolution using five common login interactive interfaces as the initial population. The effectiveness of the method was verified by design practice. The results show that the design knowledge can be summarized and reused through design rules, and the automatic intelligent aided design can be undertaken through constraint evolution, thereby elevating the level of design knowledge reuse and computer-aided intelligent innovation design.

Key words: knowledge reuse, intelligent innovation, evolutionary design, interactive interface, genetic algorithm 

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