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New IEC Research and Frameworks

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 241))

Abstract

We introduce recent research on new types of interactive evolutionary computation (IEC) applications and that on reducing IEC user fatigue. IEC is an optimization technique to embed IEC user’s subjective evaluations based on his/her domain knowledge, experiences, and preferences into several designs and has been applied to wide varieties of applications in artistic, engineering, and others for these 20 years. The approach of almost them can be said as a system optimization based on IEC user’s subjective evaluations. We review recent new research topics including an IEC as a tool for analyzing human mind, an IEC with physiological responses, and an IEC with evolutionary multi-objective optimization. We also introduce recent approaches for reducing IEC user fatigue by modeling user’s evaluation characteristics and expanding an IEC framework.

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References

  1. Aoki, K., Takagi, H.: 3-D CG Lighting with an Interactive GA. In: 1st Int. Conf. on Conventional and Knowledge-based Intelligent Electronic Systems (KES 1997), Adelaide, Australia, May 1997, pp. 296–301 (1997)

    Google Scholar 

  2. Brintrup, A.M., Takagi, H., Tiwari, A., Ramsden, J.J.: Evaluation of Sequential, Multi-Objective, and Parallel Interactive. Genetic Algorithms for Multi-Objective Optimization Problems  6, 319–354 (2006)

    Google Scholar 

  3. Ecemis, M.I., Wikel, J., Bingham, C., Bonabeau, E.: A Drug Candidate Design Environment Using Evolutionary Computation  12(5), 591–603 (2008)

    Google Scholar 

  4. Hayashida, N., Takagi, H.: Acceleration of EC convergence with Landscape Visualization and Human Intervention  1(4F), 245–256 (2002)

    Google Scholar 

  5. Henmi, S., Iwashita, S., Takagi, H.: Interactive Evolutionary Computation with Evaluation Characteristics of Multi-IEC Users. In: IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC 2006), Taipei, Taiwan, October 2006, pp. 3475–3480 (2006)

    Google Scholar 

  6. Inoue, M., Takagi, H.: Layout Algorithm for an EC-based Room Layout Planning Support System. In: Int. Conf. on Soft Computing in Industrial Applications (SMCia 2008), Muroran, Hokkaido, Japan, June 2008, pp. 165–170 (2008)

    Google Scholar 

  7. Johanson, B.: Automated Fitness Raters for the GP-Music System Technical report, Masters Degree Final Project at University of Birmingham, UK (September 1997)

    Google Scholar 

  8. Kamalian, R., Takagi, H., Agogino, A.M.: Optimized Design of MEMS by Evolutionary Multi-Objective Optimization with Interactive Evolutionary Computation. In: Genetic and Evolutionary Computation (GECCO 2004), Seattle, WA, USA, June 2004, pp. 1030–1041 (2004)

    Google Scholar 

  9. Kamalian, R.R., Yeh, R., Zhang, Y., Agogino, A.M., Takagi, H.: Reducing Human Fatigue in Interactive Evolutionary Computation through Fuzzy Systems and Machine Learning Systems. In: Int. Conf. on Fuzzy Systems (FUZZ-IEEE 2006), Vancouver, Canada, July 2006, pp. 3295–3301 (2006)

    Google Scholar 

  10. Lameijer, E.-W., Kok, J.N., Bäck, T., Ijzerman, A.P.: The Molecule Evoluator. An Interactive Evolutionary Algorithm for the Design of Drug-like Molecules. J. of Chemical Information and Modeling 46(2), 545–552 (2006)

    Article  Google Scholar 

  11. Legrand, P., Bourgeois-Republique, C., Péan, V., Harboun-Cohen, E., Levy-Vehel, J., Frachet, B., Lutton, E., Collet, P.: Interactive Evolution for Cochlear Implants Fitting  8(4), 319–354 (2007)

    Google Scholar 

  12. Nakano, N., Takagi, H.: Influence of Fitness Quantization Noise on the Performance of Interactive PSO. In: Nakano, N., Takagi, H. (eds.) IEEE Congress on Evolutionary Computation (CEC 2009), Trondheim, Norway (May 2009)

    Google Scholar 

  13. Nakaya, S.: A Modification of Scheffe’s Method for Paired Comparisons. In: Proc. of the 11th Meeting of Sensory Test, pp. 1–12 (1970) (in Japanese)

    Google Scholar 

  14. Ohsaki, M.: A Study on the Compensation for Hearing Impairment Based on Evolutionary Computation. In: Doctoral Dissertation, Kyushu Institute of Design (December 1999) (in Japanese)

    Google Scholar 

  15. Pallez, D., Collard, P., Baccino, T., Dumercy, L.: Eye-Tracking Evolutionary Algorithm to Minimize User Fatigue in IEC Applied to Interactive One-Max Problem. In: Genetic and Evolutionary Computation (GECCO 2007), London, UK, pp. 2883–2886 (2007)

    Google Scholar 

  16. Sathe, M.: VDM Verlag, Saarbrücken, Germany (2008)

    Google Scholar 

  17. Sedwell, A.N., Parmee, I.C.: Techniques for the Design of Molecules and Combinatorial Chemical Libraries. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 2435–2442 (2007)

    Google Scholar 

  18. Simons, C.L., Parmee, I.C.: A Cross-Disciplinary Technology Transfer for Search-based Evolutionary Computing: From Engineering Design to Software Engineering Design  39(5), 631–648 (2007)

    Google Scholar 

  19. Scheffé, H.: An Analysis of Variance for Paired Comparisons 47, 381–400 (1952)

    Google Scholar 

  20. Takagi, H.: Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation  89(9), 1275–1296 (2001)

    Google Scholar 

  21. Takagi, H., Ingu, T., Ohnishi, K.: Accelerating a GA Convergence by Fitting a Single-Peak Function  15(2), 219–229 (2003) (in Japanese)

    Google Scholar 

  22. Takagi, H., Takahashi, T., Aoki, K.: Applicability of Interactive Evolutionary Computation to Mental Health Measurement. In: IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC 2004), The Hague, The Netherlands, October 2004, pp. 5714–5718 (2004)

    Google Scholar 

  23. Takagi, H., Wang, S., Nakano, S.: Proposal for a Framework for Optimizing Artificial Environments Based on Physiological Feedback  24(1), 77–80 (2005)

    Google Scholar 

  24. Takagi, H., Ohsaki, M.: Interactive Evolutionary Computation-based Hearing-Aid Fitting  11(3), 414–427 (2007)

    Google Scholar 

  25. Wang, S., Takagi, H.: Improving the Performance of Predicting Users’. Subjective Evaluation Characteristics to Reduce their Fatigue in IEC  24(1), 81–85 (2005)

    Google Scholar 

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Takagi, H. (2009). New IEC Research and Frameworks. In: Fodor, J., Kacprzyk, J. (eds) Aspects of Soft Computing, Intelligent Robotics and Control. Studies in Computational Intelligence, vol 241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03633-0_4

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  • DOI: https://doi.org/10.1007/978-3-642-03633-0_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03632-3

  • Online ISBN: 978-3-642-03633-0

  • eBook Packages: EngineeringEngineering (R0)

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