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Related Experiment Videos

Combining Users' Cognition Noise with Interactive Genetic Algorithms and Trapezoidal Fuzzy Numbers for Product Color

Yan-Pu Yang1, Xing Tian1

  • 1School of Construction Machinery, Chang'an University, Xi'an 710064, China.

Computational Intelligence and Neuroscience
|September 6, 2019
PubMed
Summary
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This study introduces an advanced interactive genetic algorithm (IGA) to address user cognition differences in product color design. The new method effectively reduces noise, improving convergence and design efficiency.

Area of Science:

  • Human-Computer Interaction
  • Computational Design
  • Cognitive Science

Background:

  • Product color significantly influences brand perception and purchasing decisions.
  • Existing methods for incorporating user color preferences into design are often ineffective due to cognitive variations.
  • Interactive Genetic Algorithms (IGAs) offer a promising approach for user-involved design optimization.

Purpose of the Study:

  • To develop an advanced Interactive Genetic Algorithm (IGA) that accounts for user cognition noise in product color design.
  • To enhance the effectiveness of user preference integration in product color selection processes.
  • To improve the convergence speed and evolutionary efficiency of IGA-based design systems.

Main Methods:

  • An advanced IGA was developed, incorporating user cognition noise across different phases (cognition, intermediate, fatigue).

Related Experiment Videos

  • Trapezoidal fuzzy numbers were utilized to model the uncertainty in user evaluations.
  • A novel algorithm was designed to determine key parameters based on RGB value similarity, area proportion, and user judgment.

    • Main Results:

    • User knowledge background was found to significantly impact their cognition of product colors.
    • The proposed advanced IGA demonstrated improved convergence rates, increasing from 67.5% to 82.5%.
    • Evolutionary efficiency was enhanced, with average generations decreasing from 18.15 to 15.825.

    Conclusions:

    • The advanced IGA effectively reduces user cognition noise in interactive product color design.
    • The method significantly promotes convergence and enhances the overall evolution efficiency of the design process.
    • This approach offers a promising solution for more personalized and efficient product color design.