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JACIII Vol.16 No.3 pp. 453-461
doi: 10.20965/jaciii.2012.p0453
(2012)

Paper:

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Tournament Evaluation System Applying Win-Lose Result Presumption Considering Kansei Evaluation by Multiple People

Hiroshi Takenouchi*, Masataka Tokumaru**,
and Noriaki Muranaka**

*Graduate School of Science and Engineering, Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan

**Faculty of Engineering Science, Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan

Received:
June 22, 2011
Accepted:
February 3, 2012
Published:
May 20, 2012
Creative Commons License
Keywords:
interactive genetic algorithm, tournament evaluation, win-lose result presumption
Abstract
We describe an Interactive Genetic Algorithm (IGA) with tournament evaluation for applying win-lose results obtained from an evaluation using multiple people. In our previous study, we developed an IGA with tournament evaluation as a basic model for evaluating candidate solutions using votes from multiple people [13]. However, tournament evaluation requires that IGA users evaluate the same candidate solution multiple times. Therefore, our previous method can reduce a user’s motivation for evaluating the solution. In addition, the number of users participating in a vote may decrease because of the decreasedmotivation to evaluate. To overcome this difficulty, we propose the application of a win-lose result presumption based on the tournament evaluation records of multiple people. When a system-based presumption is possible, the win-lose result presumption automatically determines the preferred and non-preferred candidates in each round. This method can reduce the number of times that users need to evaluate the same candidate solution. The effectiveness of the proposed method is verified using a numerical simulation that employs multiple numerical evaluation agents instead of human evaluators. The simulation results show an initial convergent improvement with the proposed method.
Cite this article as:
H. Takenouchi, M. Tokumaru, and N. Muranaka, “Tournament Evaluation System Applying Win-Lose Result Presumption Considering Kansei Evaluation by Multiple People,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.3, pp. 453-461, 2012.
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References
  1. [1] H. Takagi, “Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation,” Proc. IEEE, Vol.89, No.9, pp. 1275-1296, 2001.
  2. [2] S. H. Lee, A. Harada, and P. J. Stappers, “Pleasure with Products: Design based Kansei,” Pleasure with Products: Beyond usability, pp. 219-229, 2002.
  3. [3] S.-B. Cho and J.-Y. Lee, “A Human-Oriented Image Retrieval System Using Interactive Genetic Algorithm,” IEEE Trans. Systems, Man, and Cybernetics Part A: Systems and Humans, Vol.32, No.3, pp. 452-458, 2002.
  4. [4] H.-S. Kim and S.-B. Cho, “Development of an IGA-based fashion design aid system with domain specific knowledge,” Proc. IEEE Int. Conf. on Systems, Man and Cybernetics (IEEE SMC 1999), Vol.3, pp. 663-668, 1999.
  5. [5] M. Tokumaru, N. Muranaka, and S. Imanishi, “Virtual Stylist Project – Examination of Adapting Clothing Search System to User’s Subjectivity with Interactive Genetic Algorithms –,” 2003 IEEE Congress on Evolutionary Computation (IEEE CEC 2003), pp. 1036-1043, 2003.
  6. [6] Y. Bamba, J. Kotani, andM. Hagiwara, “An Interior Layout Support System with Interactive Evolutionary Computation using Evaluation Agents,” Joint 5th Int. Conf. on Soft Computing and Intelligent Systems and 11th Int. Symposium on Advanced Intelligent Systems (SCIS&ISIS 2004), WE-2-4, pp. 1-6, 2004.
  7. [7] M. Miki, H. Orita, and T. Hiroyasu, “Design of Sign Sounds using Interactive Genetic Algorithm,” Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics (IEEE SMC 2006), pp. 93-98, 2006.
  8. [8] H. Takagi and M. Ohsaki, “Interactive Evolutionary Computationbased Hearing Aid Fitting,” IEEE Trans. Evolutionary Computation, Vol.11, No.3, pp. 414-427, 2007.
  9. [9] H. Inoue and H.Miyagoshi, “Behavior Evolution of Pet Robots with Human Interaction,” Proc. Second Int. Conf. on Innovative Computing, Information and Control, pp. 23-26, 2007.
  10. [10] K. Tagawa, H. Kawamura, and A. Tani, “Architectural Interior Design Support System by Interactive Evolutionary Computing – Consensus-Building Support System on Texture Selection of Interior by Using Pair-wise Comparison Matrix –,” Proc. the Symposium on Computer Technology of Information, Systems and Applications, Vol.26, pp. 43-48, 2008 (in Japanese).
  11. [11] M. Miki, T. Hiroyasu, and H. Tomioka, “Validity of the Consensus Building System using a Parallel Distributed Interactive Genetic Algorithm,” Trans. the Japanese Society for Artificial Intelligence, Vol.20, No.4, pp. 289-296, 2005 (in Japanese).
  12. [12] S. Henmi, S. Iwashita, and H. Takagi, “Interactive Evolutionary Computation with Evaluation Characteristics of Multi-IEC Users,” Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics (IEEE SMC 2006), pp. 3475-3480, 2006.
  13. [13] H. Takenouchi, M. Tokumaru, and N. Muranaka, “Tournament Evaluation System Considering Multiple People’s Kansei Evaluation,” J. of Kansei Engineering International, Vol.9, No.2, pp. 43-50, 2010.
  14. [14] H. Takenouchi, M. Tokumaru, and N. Muranaka, “Interactive Genetic Algorithm with Tournament Evaluation Applying Paired Preference Test by Multiple People,” J. of Japan Society for Fuzzy Theory and Intelligent Informations, Vol.23, No.1, pp. 38-53, 2011 (in Japanese).

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