Interactive Genetic Algorithm for Human-Computer Interface Layout Design System
Authors: 
Mengyao Yu, Changhua He, Lijun ShiAuthors Info & Claims
Pages 43 - 48
Abstract
Purpose: This study proposes an interface layout design method based on an interactive genetic algorithm (IGA) to improve the user experience of the human-computer interface (HCI). Methods: Firstly, the interactive genetic algorithm constructs an interactive interface design model; secondly, the genetic algorithm for the human-computer interaction interface is implemented by coding the interface design typography component library. Finally, I witnessed the practicality and feasibility of the algorithm proposed in this study. Results: The algorithm is adaptable and implementable, showing that the user experience can be rapidly improved through the typographic design of human-computer interaction interfaces. Conclusion: By constantly iterating the model of the component library coding method for HCI layout design, it is possible to initially implement the HCI layout design and genetic algorithm with severe difficulty and complexity to improve the interface design user experience.
Additional Keywords and Phrases
Interactive Genetic Algorithm, Human-Computer Interface, Layout Design, Optimisation System
References
[1]
Cheng, S., & Dey, A. K. (2019. I see, you design: user interface intelligent design system with eye tracking and interactive genetic algorithm. CCF Transactions on Pervasive Computing and Interaction, 1(3), 224–236. https://doi.org/10.1007/s42486-019-00019-w
[2]
Diego-Mas, J. A., Garzon-Leal, D., Poveda-Bautista, R., & Alcaide-Marzal, J. 2019. User-interfaces layout optimization using eye-tracking, mouse movements and genetic algorithms. Applied Ergonomics, 78, 197–209. https://doi.org/10.1016/j.apergo.2019.03.004
[3]
Deng, L., Zhou, F., & Zhang, Z. 2022. Interactive genetic color matching design of cultural and creative products considering color image and visual aesthetics. Heliyon, 8(9), e10768. https://doi.org/10.1016/j.heliyon.2022.e10768
[4]
Gan, S., Zhuang, Q., & Gong, B. 2022. Human-computer interaction based interface design of intelligent health detection using PCANet and multi-sensor information fusion. Computer Methods and Programs in Biomedicine, 216, 106637. https://doi.org/10.1016/j.cmpb.2022.106637
[5]
Gurcan, F., Cagiltay, N. E., & Cagiltay, K. 2021. Mapping human–computer interaction research themes and trends from its existence to today: A topic modeling-based review of past 60 years. International Journal of Human–Computer Interaction, 37(3), 267-280.https://doi.org/10.1080/10447318.2020.1819668
[6]
Gypa, I., Jansson, M., Wolff, K., & Bensow, R. 2021. Propeller optimization by interactive genetic algorithms and machine learning. Ship Technology Research, 1–16. https://doi.org/10.1080/09377255.2021.1973264
[7]
Xu, L. 2021. Research on computer interactive optimization design of power system based on genetic algorithm. Energy Reports, 7, 1–13. https://doi.org/10.1016/j.egyr.2021.10.085
[8]
Shang, J., Liu, H., & Li, W. 2022. Human-Computer Interaction of Networked Vehicles Based on Big Data and Hybrid Intelligent Algorithm. Wireless Communications and Mobile Computing, 2022, 1–13. https://doi.org/10.1155/2022/5281132
[9]
Liu, J., Mei Choo Ang, Jun Kit Chaw, Kor, A.-L., & Kok Weng Ng. 2023. Emotion assessment and application in human–computer interaction interface based on backpropagation neural network and artificial bee colony algorithm. Expert Systems with Applications, 232, 120857–120857. https://doi.org/10.1016/j.eswa.2023.120857
[10]
Nazar, M., Alam, M. M., Yafi, Dr. E., & Mazliham, M. S. 2021. A Systematic Review of Human-Computer Interaction and Explainable Artificial Intelligence in Healthcare with Artificial Intelligence Techniques. IEEE Access, 9, 1–1. https://doi.org/10.1109/access.2021.3127881
[11]
Fu, Q., & Lv, J. 2020. Research on application of cognitive-driven human-computer interaction. Am. Sci. Res. J. Eng. Technol. Sci, 64, 9-27.http://asrjetsjournal.org/
[12]
Hai, C. 2020. The Fractal Artistic Design Based on Interactive Genetic Algorithm. Computer-Aided Design and Applications, 17(S2), 35–45. https://doi.org/10.14733/cadaps.2020.s2.35-45
[13]
Qi, J., Jiang, G., Li, G., Sun, Y., & Tao, B. 2019. Intelligent human-computer interaction based on surface EMG gesture recognition. Ieee Access, 7, 61378-61387.https://ieeexplore.ieee.org/abstract/document/8706969
[14]
Soui, M., Chouchane, M., Mkaouer, M. W., Kessentini, M., & Ghedira, K. 2019. Assessing the quality of mobile graphical user interfaces using multi-objective optimization. Soft Computing, 24(10), 7685–7714. https://doi.org/10.1007/s00500-019-04391-8
[15]
Wang, T., & Zhou, M. 2020. A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction. International Journal of Industrial Ergonomics, 76, 102901. https://doi.org/10.1016/j.ergon.2019.102901
Index Terms
- Interactive Genetic Algorithm for Human-Computer Interface Layout Design System
Recommendations
The University of Alberta user interface management system
In this paper the design and implementation of the University of Alberta user interface management system (UIMS) is discussed. This UIMS is based on the Seeheim model of user interfaces, which divides the user interface into three separate components. ...
The University of Alberta user interface management system
SIGGRAPH '85: Proceedings of the 12th annual conference on Computer graphics and interactive techniquesIn this paper the design and implementation of the University of Alberta user interface management system (UIMS) is discussed. This UIMS is based on the Seeheim model of user interfaces, which divides the user interface into three separate components. ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2023
467 pages
ISBN:9798400716966
DOI:10.1145/3661638
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 June 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AISNS 2023
AISNS 2023: 2023 International Conference on Artificial Intelligence, Systems and Network Security
December 22 - 24, 2023
Mianyang, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 16Total Downloads
- Downloads (Last 12 months)16
- Downloads (Last 6 weeks)5
Reflects downloads up to 23 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format