Practically Applying Interactive Genetic Algorithms to Customers’ Designs on a Customizable C2C Framework: Entrusting Select Operations to IGA Users

  • Fang-Cheng Hsu
  • Ming-Hsiang Hung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


We propose a customizable C2C (customer to customer) framework to fully utilize interactive genetic algorithms (IGA) and to discover the potential capabilities of IGAs in customer designs. Traditionally, IGA users assign fitness to each chromosome. No matter the rating or ranking of the assignments, the traditional methods were unnatural, especially when IGAs were applied to customers’ designs. In this study, we find that allowing IGA users to directly select chromosomes into the mating pool according to their hidden fitness function(s) is not only a natural way to implement the select operations of IGA, but is also more effective. We call the model where parts of select operations are manipulated by users, the SIGA model. Preventing fatigue, however, is the most important challenge in IGA. The OIGA (Over-sampling IGA) model has been extremely effective at decreasing user fatigue. To verify the performance of the proposed SIGA, we conduct a case study and use the OIGA model as a benchmark. The results of the case study show that the proposed SIGA model is significantly more effective than the IOGA model.


Mating Pool Mineral Water Bottle Fitness Assignment Select Operation Fatigue Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Takagi, H.: Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation. Proceedings of IEEE 89(9), 1275–1296 (2001)CrossRefGoogle Scholar
  2. 2.
    Urban, G.L., von Hippel, E.: Lead User Analyses for the Development of New Industrial Products. Management Science 34(5), 569–582 (1988)CrossRefGoogle Scholar
  3. 3.
    Thomke, S., von Hippel, E.: Customers as Innovators - a New Way to Create Value. Harvard Business Review 80, 74–81 (2002)Google Scholar
  4. 4.
    Dahan, E., Hauser, J.R.: The Virtual Customer. Journal of Product Innovation Management 19(5), 332–354 (2001)CrossRefGoogle Scholar
  5. 5.
    Olivier, T., Hauser, J.R., Simester, D.I.: Polyhedral Methods for Adaptive Choice- Based Conjoint Analysis. Journal of Marketing Research 41(1), 116–131 (2004)CrossRefGoogle Scholar
  6. 6.
    Olivier, T., Simester, D.I., Hauser, J.R., Dahan, E.: Fast Polyhedral Adaptive Conjoint Estimation. Marketing Science 22(3), 273–303 (2003)CrossRefGoogle Scholar
  7. 7.
    Caldwell, C., Johnston, V.S.: Tracking a Criminal Suspect through Face-Space with a Genetic Algorithm. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 416–421. Morgan Kaufmann, San Francisco (1991)Google Scholar
  8. 8.
    Nishio, K., Murakami, M., Mizutani, E., Honda, N.: Fuzzy Fitness Assignment in an Interactive Genetic Algorithm for a Cartoon Face Search. In: Sanchez, E., Shibata, T., Zadeh, I.A. (eds.) Genetic Algorithms and Fuzzy Logic Systems - Soft Computing Perspectives, pp. 175–191. World Scientific Publishing, Singapore (1997)Google Scholar
  9. 9.
    Hsu, F.C., Chen, J.S.: A Study on Multi Criteria Decision Making Model: Interactive Genetic Algorithms Approach. In: Proceedings of the 1999 International Conference on SMC, Tokyo, Japan, pp. 634–639 (1999)Google Scholar
  10. 10.
    Hsu, F.C., Huang, P.: Providing an appropriate search space to solve the fatigue problem in interactive evolutionary computations. New Generation Computing 23(2), 114–126 (2005)CrossRefGoogle Scholar
  11. 11.
    Keeney, R.L.: Value-Focused Thinking: A path to Creative Decision-Making. Harvard University Press, Cambridge (1992)Google Scholar
  12. 12.
    Hung, M.H., Hsu, F.C.: Accelerating Interactive Evolutionary Computation Convergence Pace by Using Over-sampling Strategy. In: The Fourth IEEE International Workshop on Soft Computing as Trans-disciplinary Science and Technology, Muroran, Japan, May 25-27 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fang-Cheng Hsu
    • 1
  • Ming-Hsiang Hung
    • 2
  1. 1.Department of Information ManagementAletheia University 
  2. 2.Graduate School of Management ScienceAletheia University 

Personalised recommendations