A Simple and Automatic Typesetting Method Based on BM Value of Interface Aesthetics and Genetic Algorithm

被引:3
|
作者
Wang, Liuqing [1 ]
Xue, Chengqi [1 ]
机构
[1] Southeast Univ, Sch Mech Engn, Dept Ind Design, Nanjing 211189, Peoples R China
关键词
Interface aesthetics; Genetic algorithm; Automatic typesetting;
D O I
10.1007/978-3-030-80091-8_111
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
After more than ten years of development, the evaluation system of interface aesthetics has been gradually applied to the evaluation of the layout of various interface elements, but it is still difficult to guide the actual interface design. In this paper, starting from Measure of Balance, one of the evaluation indexes of interface aesthetics, and combining with the genetic algorithm, a simple automatic typesetting algorithm was constructed on Grasshopper (Rhino) Visual programming platform, and the results of its optimization were evaluated with satisfaction. The evaluation results show that this automatic typesetting algorithm produces some satisfactory results, and it can be better applied to the automatic typesetting of simple graphics and text. However, for complex graphic typesetting, the automatic typesetting algorithm needs to be further optimized, because this algorithm is only based on the BM value of interface aesthetics. When the BM value is maximum, only the balance degree is optimal, which does not represent that the overall interface aesthetics is optimal. Therefore, it is necessary to introduce other interface aesthetics evaluation indexes to supplement and build a more mature comprehensive index of interface aesthetics before it can be better applied to the actual interface design.
引用
收藏
页码:931 / 938
页数:8
相关论文
共 50 条
  • [1] Evaluation and Improvement of Interface Aesthetics with an Interactive Genetic Algorithm
    Bauerly, Michael
    Liu, Yili
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2009, 25 (02) : 155 - 166
  • [2] An automatic scheduling method for weaving enterprises based on genetic algorithm
    Wang, Jing-An
    Pan, Ruru
    Gao, Weidong
    Wang, Hongbo
    JOURNAL OF THE TEXTILE INSTITUTE, 2015, 106 (12) : 1377 - 1387
  • [3] A Genetic Algorithm-Based Method for the Automatic Reduction of Reaction Mechanisms
    Sikalo, N.
    Hasemann, O.
    Schulz, C.
    Kempf, A.
    Wlokas, I.
    INTERNATIONAL JOURNAL OF CHEMICAL KINETICS, 2014, 46 (01) : 41 - 59
  • [4] A Genetic Algorithm Based Motor Controller System Automatic Layout Method
    Cao, Han
    Ning, Puqi
    Wen, Xuhui
    Yu, Tianshu
    2019 10TH INTERNATIONAL CONFERENCE ON POWER ELECTRONICS AND ECCE ASIA (ICPE 2019 - ECCE ASIA), 2019,
  • [5] Automatic Design Method of Dynamic Systems Based on Hungarian Algorithm and Genetic Programming
    Li Shaobo
    Yang Guanci
    Xie Qingsheng
    2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31, 2008, : 2294 - 2297
  • [6] General method for automatic on-line beamline optimization based on genetic algorithm
    Xi, Shibo
    Borgna, Lucas Santiago
    Du, Yonghua
    JOURNAL OF SYNCHROTRON RADIATION, 2015, 22 : 661 - 665
  • [7] A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation
    Hammouche, Kamal
    Diaf, Moussa
    Siarry, Patrick
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2008, 109 (02) : 163 - 175
  • [8] Genetic Algorithm for Automatic Negotiation Based on Agent
    Niu, Xiaotai
    Wang, Su
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 3834 - +
  • [9] An efficient method of genetic algorithm for text clustering based on singular value decomposition
    Song, Wei
    Park, Soon Cheol
    2007 CIT: 7TH IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY, PROCEEDINGS, 2007, : 53 - 58
  • [10] Automatic Generation Method of Optimization Scheme for Orderly Power Utilization Based on Genetic Algorithm
    Huang Jian-jun
    Zuo Qing-lin
    Mu Fu-lin
    2013 IEEE 11TH INTERNATIONAL CONFERENCE ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING (DASC), 2013, : 72 - 77