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Providing an appropriate search space to solve the fatigue problem in interactive evolutionary computation

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Abstract

The user fatigue problem in interactive evolutionary computation (IEC) is a complex and interesting issue. If the IEC search space is created from the experience or knowledge of domain experts rather than from users values, it causes two potential problems which lead to fatigue problems in IEC: 1) inefficiency and 2) boredom. Therefore, we propose a customer values-based IEC model, solving the fatigue problem by avoiding the potential problems. A case study involving the design of mineral water bottles was used to verify the anti-fatigue capability of the users when using the proposed model. For comparison with the traditional domain knowledge-based model, we built two IEC systems, a customer values-based system and a traditional system, and conducted a user burden test and a system efficiency test over a two-week period. The results of both tests show that our proposed system performed better than the traditional system in designing mineral water bottles.

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References

  1. Biles, J. A., Anderson, P. G. and Loggi, L. W., “Neural Network Fitness Functions for a Musical IGA,” inProceedings of the IIA’96/SOCO’96. Int. ICSC Symposia on Intelligent Industrial Automation And Soft Computing, pp. B39–44, 1996.

  2. Caldwell, C. and Johnston, V. S., “Tracking a Criminal Suspect through ‘Face-Space’ with a Genetic Algorithm,” inProceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, California, pp. 416–421, 1991.

    Google Scholar 

  3. Dawkins, R., “The Evolution of Evolvability,”Artificial Life, Addison Wesley, pp. 201–220, 1989.

  4. Hayashida, H. and Takagi, H., “Accelerating of EC Convergence with Landscape Visualization and Human Intervention,”Applied Soft Computing, 1, 4, pp. 245–256, 2002.

    Article  Google Scholar 

  5. Hsu, F. C. and Chen, J. S., “A Study on Multi Criteria Decision-making Model: Interactive Genetic Algorithms Approach,” inProceedings of the IEEE Int. Conf. on System, Man, and Cybernetics (SMC’99) 3, (Tokyo, Japan), pp. 634–639, 1999.

  6. Ingu, T. and Takagi, H., “Accelerating a GA Convergence by Fitting a Single-peak Function,” inProceedings of the IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE’99), pp. 1415–1420, 1999.

  7. Keeney, R. L.,Value-Focused Thinking: A Path to Creative Decision-Making, Harvard University Press, Cambridge, Massachusetts, 1992.

    Google Scholar 

  8. Keeney, R. L., “Creativity in Decision Making with Value-Focused Thinking,”Sloan Management Review, 35, 4, pp. 33–41, 1994.

    Google Scholar 

  9. Keeney, R. L., “The Value of Internet Commerce to the Customer,”Management Science, 45, 4, pp. 533–542, 1999.

    Article  Google Scholar 

  10. Kim, H. S. and Cho, S. B., “Application of Interactive Genetic Algorithm to Fashion Design,”Engineering Applications of Artificial Intelligence, 13, pp. 635–644, 2000.

    Article  Google Scholar 

  11. Lee, J. Y. and Cho, S. B., “Sparse Fitness Evaluation for Reducing User Burden in Interactive Genetic Algorithm,” inProceedings of the FUZZ-IEEE’99, II, pp. 998–1003, 1999.

  12. Nishio, K., Murakami, M., Mizutani, E. and Honda, N., “Efficient Fuzzy Fitness Assignment Strategies in an Interactive Genetic Algorithm for Cartoon Face Search,” inProceedings of the 6th Int. Fuzzy Systems Association World Congress (IFSA’95), 1, pp. 173–176, 1995.

  13. Nishio, K., Murakami, M., Mizutani, E. and Honda, N., “Fuzzy Fitness Assignment in an Interface Genetic Algorithm for a Cartoon Face Search,” inGenetic Algorithm and Fuzzy Logic Systems, Soft Computing Perspectives (Sanchez, E., Shibata, T. and Zadeh, L. A. eds.), vol. 7 of Advances in Fuzzy Systems-Applications and Theory, pp. 175–192, 1997.

  14. Olivier, T., Simester, D. I., Hauser, J. R. and Dahan, E., “Fast Polyhedral Adaptive Conjoint Estimation,”Marketing Science, 22, 3, pp. 273–303, 2003.

    Article  Google Scholar 

  15. Olivier, T., Hauser, J. R. and Simester, D. I., “Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis,”Journal of Marketing Research, 3, 1, pp. 116–131, 2004.

    Google Scholar 

  16. Ohsaki, M., Takagi, H. and Ohya, K., “An Input Method Using Discrete Fitness Values for Interactive GA,”Journal of Intelligent and Fuzzy Systems, 6, pp. 131–145, 1998.

    Google Scholar 

  17. Ohsaki, M., Takagi, H. and Ingu, T., “Methods to Reduce the Human Burden of Interactive Evolutionary Computation,” inProceedings of the Asia Fuzzy System Symposium (AFSS’98), pp. 495–500, June, 1998.

  18. Ohsaki, M. and Takagi, H., “Improvement of Presenting Interface by Predicting the Evaluation Order to Reduce the Burden of Human Interactive EC Operators,” inProceedings of the IEEE Int. Conf. on System, Man, Cybernetics (SMC’98), pp. 1284–1289, Oct., 1998.

  19. Parmee, I. C., Cvetkovic, C., Watson, A. H. and Bonham, C. R., “Multi-objective Satisfaction within an Interactive Evolutionary Design Environment,”Journal of Evolutionary Computation, 8, 2, pp. 197–222, 2000.

    Article  Google Scholar 

  20. Smith, J. R., “Designing Biomorphs with an Interactive Genetic Algorithm,” inProceedings of the 4th International Conference on Genetic Algorithms, pp. 535–538, 1991.

  21. Takagi, H., “Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation,”in Proceedings of IEEE, 89, 9, pp. 1275–1296, 2001.

  22. Takagi, H., “Interactive GA for System Optimization: Problems and Solution,” inProceedings of the 4th European Congress on Intelligent Techniques and Soft Computing (EUFIT’96), pp. 1440–1444, 1996.

  23. Takagi, H., “Active User Intervention in an EC Search,” inProceedings of the 5th Joint Conf. on Information Sciences (JCIS2000), pp. 995–998, 2000.

  24. Takagi, H., “System Optimization without Numerical Target,” inProceedings of the 1996 Biennial Conf. of the North American Fuzzy Information Processing Society (NAFIPS’96), pp. 351–354, June, 1996.

  25. Takagi, H. Ohya, K. and Ohsaki, M., “Improvement of Input Interface for Interactive GA and Its Evaluation,” inProceedings of the Int. Conf. on Soft Computing (IIZUKA’96), pp. 490–493, 1996.

  26. Takagi, H. and Ohya, K., “Discrete Fitness Values for Improving the Human Interface in an Interactive GA,” inProceedings of the IEEE 3rd Int. Conf. onEvolutionary Computation (ICEC’96), pp. 109–112, 1996.

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Authors and Affiliations

  1. Department of Information Management, Aletheia University, 32 Chen Li Street, Tamsui, 251, Taipei, Taiwan

    Fang -Cheng Hsu

  2. Information Technology Total Services 5F, 19-8 SanChong Road, Nankang, 115, Taipei, Taiwan

    Peter Huang

Authors
  1. Fang -Cheng Hsu
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  2. Peter Huang
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Correspondence to Fang -Cheng Hsu.

Additional information

Fang-Cheng Hsu, Ph.D.: He received his M.S. degree from Tamkang University, Taiwan, in 1985 and Ph.D. degree from Department of Information Management, National Central University, Taiwan, in 2000. He is an associate professor of Information Management at Aletheia University in Taiwan. Prior to this, he was on the faculty at Tamsui Oxford College. His current research interests include interactive evolutionary computation and evolutionary computation-based decision support systems.

Peter Huang: He received his B.A. degree from Department of Information Management, Aletheia University, Taiwan, in 1997, and M.B.A. degree from Graduate Institute of Management Science, Aletheia University, Taiwan, in 2003. He is on staff at Information Technology Total Services and is a part-time lecturer at Aletheia University. His current research interests are decision analysis and interactive evolutionary computation.

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Hsu, F.C., Huang, P. Providing an appropriate search space to solve the fatigue problem in interactive evolutionary computation. New Gener Comput 23, 115–127 (2005). https://doi.org/10.1007/BF03037489

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  • DOI: https://doi.org/10.1007/BF03037489

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