Skip to main content

Sunglasses Styling Optimization System Based on User Interactions

  • Conference paper
  • First Online:
  • 736 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 206))

Abstract

Considering sunglasses’ design features like large capacity, short period, quick modification, being difficult to accurately capture the user demand and so on, this paper achieved the optimal design of sunglasses form, and developed a prototype system based on interactive genetic algorithm, which realized the optimization mechanism from three aspects, as lens form coding, visualized population construction and users’ interactive evaluation model design. The algorithm program is developed based on three-dimensional design platform Solid works, and running as macro. The software extracts parameters from the user defined model and automatically generates new designs with the parameters, and then displays them for user’s evaluation which drives the optimization process to go circularly.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Takagi H (2001) Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc IEEE 89(9):1275–1296

    Article  Google Scholar 

  2. Parmee IC, Bonham CR (2000) Towards the support of innovative conceptual design through interactive designer/evolutionary computing strategies. Artif Intell Eng Des Anal Manuf 14(1):3–16

    Article  Google Scholar 

  3. Kim HS, Cho SB (2000) Application of interactive genetic algorithm to fashion design. Eng Appl Artif Intell 13(6):635–644

    Article  MathSciNet  Google Scholar 

  4. Gu ZY, Tang MX, Frazer JH (2006) Capturing aesthetic intention during interactive evolution. Comput Aided Des 38(3):224–237

    Article  Google Scholar 

  5. Cho SB (2004) Emotional image and musical information retrieval with interactive genetic algorithm. Proc IEEE 92(4):702–711

    Article  Google Scholar 

  6. Gong DW, Hao GS (2007) Interactive genetic algorithms theory and applications, vol 77. National Defense Press, Beijing, pp 8–10

    Google Scholar 

  7. Biles JA, Anderson PG, Loggi LW (1996) Neural network fitness functions for a musical IGA. Int Symp Intell Ind Autom Soft Comput 90:39–44

    Google Scholar 

  8. Wang SF, Wang XF, Xue J (2005) An improved interactive genetic algorithm incorporating relevant feedback. In: Proceedings of 2005 international conference on machine learning and cybernetics, vol 9. Guangzhou, pp 2996–3001

    Google Scholar 

  9. Wang LH (2007) A comparison of three fitness prediction strategies for interactive genetic algorithms. J Inf Sci Eng 23(2):605–616

    Google Scholar 

  10. Sugimoto F, Yoneyama M (2002) Hybrid fitness assignment strategy in IGA: a method to compose fitness. In: Proceedings of the 2002 IEEE workshop on multimedia signal processing. ST Thomas, Virgin Islands, pp 284–287

    Google Scholar 

  11. Rasheed K (2000) Informed operators: speeding up genetic-algorithm-based design optimization using reduced models. Proc Genet Evol Comput Conf 55:628–635

    Google Scholar 

  12. Abboud K, Schoenauer M (2002) Surrogate deterministic mutation. Artif Evol 99(1):103–115

    Google Scholar 

  13. Merz P, Freisleben B (2000) Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans Evol Comput 4:337–352

    Article  Google Scholar 

  14. Lee JH, Cho SB (2002) Analysis of direct manipulation in interactive evolutionary computation on fitness landscape. In: Proceedings of the 2002 congress on evolutionary computation, vol 87. Honolulu, pp 460–465

    Google Scholar 

Download references

Acknowledgments

The paper is sponsored by Chinese National Natural Science Fund (No. 60975048), Zhejiang Natural Science Fund (No. Y1111111), Zhejiang Public Welfare Technology Research Project (No. 2010C31065) and Wenzhou Science & Technology Program (No. G20090098, G20100195). Many thanks to government’s generosity to our research work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haiying Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this paper

Cite this paper

Li, H., He, X., Wu, J., Liu, X. (2013). Sunglasses Styling Optimization System Based on User Interactions. In: Du, W. (eds) Informatics and Management Science III. Lecture Notes in Electrical Engineering, vol 206. Springer, London. https://doi.org/10.1007/978-1-4471-4790-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4790-9_11

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4789-3

  • Online ISBN: 978-1-4471-4790-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics