Interactive Evolutionary Computation Framework and the On-Chance Operator for Product Design

  • Leuo-hong Wang
  • Meng-yuan Sung
  • Chao-fu Hong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


Traditionally, product design problem is usually solved by means of the conjoint analysis methods. However, the conjoint analysis methods suffer from evaluation fatigue. An interactive evolutionary computation (IEC) framework for product design has been thus proposed in this paper. The prediction module taking care of evaluation fatigue is the main part of this framework. In addition, since the evaluation function of product design is an additive utility function, designing operators which heavily utilizes the prediction results becomes possible. The on-chance operator is thus defined in this paper as well. The experimental results indicated the on-chance operator can speed up IEC and improve the quality of solution at the same time.


Product Design Attribute Level Conjoint Analysis Prediction Module Evaluation Fatigue 
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.


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  1. 1.
    Krishnan, V., Ulrich, K.T.: Product development decisions: A review of the literature. Manage. Sci. 47, 1–21 (2001)CrossRefGoogle Scholar
  2. 2.
    Shocker, A.D., Srinivasan, V.: Multiattribute approaches for product concept evaluation and generation: A critical review. Journal of Marketing Research 16, 158–180 (1979)CrossRefGoogle Scholar
  3. 3.
    Green, P.E., Srinivasan, V.: Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing 54, 3–19 (1990)CrossRefGoogle Scholar
  4. 4.
    Kohli, R., Krishnamurti, R.: Optimal product design using conjoint analysis: computational complexity and algorithms. European Journal of Operational Research 40, 186–195 (1989)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Johnson, R.M.: Adaptive conjoint analysis. In: Sawtooth Software Conference Proceedings, pp. 253–265 (1987)Google Scholar
  6. 6.
    Toubia, O., Simester, D., Hauser, J., Daha, E.: Fast polyhedral adaptive conjoint estimation. Marketing Science 22, 273–303 (2003)CrossRefGoogle Scholar
  7. 7.
    Takagi, H.: Interactive evolutionary computation: Fusion of the capacities of ec optimization and human evaluation. Proceedings of the IEEE 89, 1275–1296 (2001)CrossRefGoogle Scholar
  8. 8.
    Keeney, R.L., Raifa, H.: Decisions with multiple objectives: preferences and valuetradeoffs. John Wiley, New York (1976)Google Scholar
  9. 9.
    Hedayat, A., Sloane, N., Stufken, J.: Orthogonal Arrays: Theory and Application. Springer, New York (1999)MATHGoogle Scholar
  10. 10.
    Saez, Y., Isasi, P., Segovia, J., Hernandez, J.C.: Reference chromosome to overcome user fatigue in iec. New Generation Computing 23, 129–142 (2005)CrossRefGoogle Scholar
  11. 11.
    Saez, Y., Isasi, P., Segovia, J.: Interactive Evolutionary Computation algorithms applied to solve Rastrigin test functions. In: Soft Computing as Transdisciplinary Science and Technology, pp. 682–691. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Llorá, X., Sastry, K., Goldberg, D., Gupta, A., Lakshmi, L.: Combating user fatigue in igas: Partial ordering, support vector machines, and synthetic fitness. In: ACM Genetic and Evolutionary Computation Conference (GECCO 2005), pp. 1363–1371. ACM press, New York (2005)CrossRefGoogle Scholar
  13. 13.
    Biles, J.A., Anderson, P.G., Loggi, L.W.: Neural network fitness functions for a musical iga. In: International ICSC Symposium on Intelligent Industrial Automation and Soft Computing (1996)Google Scholar
  14. 14.
    Burton, A., Vladimirova, T.: Genetic algorithm utilising neural network fitness evaluation for musical composition. In: Smith, G.D., Albrecht, R.F., Steele, N.C., (eds.) Proceedings of the 1997 International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 220–224 (1997)Google Scholar
  15. 15.
    Johanson, B., Poli, R.: GP-music: An interactive genetic programming system for music generation with automated fitness raters. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 181–186. Morgan Kaufmann, San Francisco (1998)Google Scholar
  16. 16.
    Dozier, G.V.: Evolving robot behavior via interactive evolutionary computation: from real-world to simulation. In: Proceedings of the 2001 ACM Symposium on Applied Computing (SAC), pp. 340–344 (2001)Google Scholar
  17. 17.
    Simon, H.A.: The New Science of Management Decision. Prentice-Hall, Upper Saddle River (1977)Google Scholar
  18. 18.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization&Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Leuo-hong Wang
    • 1
  • Meng-yuan Sung
    • 2
  • Chao-fu Hong
    • 1
  1. 1.Evolutionary Computation Laboratory, Department of Information ManagementAletheia UniversityTaiwan
  2. 2.Graduate School of Management SciencesAletheia UniversityTaiwan

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