Reducing Human Fatigue in Interactive Evolutionary Computation through Fuzzy Systems and Machine Learning Systems
Raffi R. Kamalian, Reic Yeh, Ying Zhang, Alice M. Agogino, and Hideyuki Takagi: Reducing Human Fatigue in Interactive Evolutionary Computation through Fuzzy Systems and Machine Learning Systems. 2006 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2006), pp.3295-3301, Vancouver, Canada (July 16-21, 2006).
We describe two approaches to reducing human fatigue in interactive evolutionary computation (IEC). A predictor function is used to estimate the human user's score, thus reducing the amount of effort required by the human user during the evolution process. The fuzzy system and four machine learning classifier algorithms are presented. Their performance in a real-world application, the IEC-based design of a micromachine resonating mass, is evaluated. The fuzzy system was composed of four simple rules, but was able to accurately predict the user's score 77% of the time on average. This is equivalent to a 51 % reduction of human effort compared to using IEC without the predictor. The four machine learning approaches tested were k-nearest neighbors, decision tree, AdaBoosted decision tree, and support vector machines. These approaches achieved good accuracy on validation tests, but because of the great diversity in user scoring behavior, were unable to achieve equivalent results on the user test data.
D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman, Boston, MA, 1989.
H. Takagi, "Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation", Proceedings of the IEEE, vol. 89, no. 9, pp. 1275-1296, 2001. http://dx.doi.org/10.1109/5.949485
Z. Gu, M. Tang, and J.H. Frazer, "Capturing Aesthetic Intention during Interactive Evolution", Computer-Aided Design, 1-14, 2005. http://dx.doi.org/10.1016/j.cad.2005.10.008
N. Zhou, B. Zhu, A. M. Agogino, and K.S.J. Pister, "Evolutionary Synthesis of MEMS (Micro Electronic Mechanical Systems) Design", Proceedings of the Artificial Neural Networks in Engineering (ANNIE2001), pp. 197-202, 2001.
R. Kamalian, H. Takagi, and A.M. Agogino, "Optimized Design of MEMS by Evolutionary Multi-objective Optimization with Interactive Evolutionary Computation", Proceedings of GECCO 2004, Genetic and Evolutionary Computation Conference, pp. 1030-1041, 2004.
R. Kamalian, Y. Zhang, H. Takagi, and A.M. Agogino, "Reduced Human Fatigue in Interactive Evolutionary Computation for Micromachine Design", Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, August 18-21 2005, pp. 5666-5671. http://dx.doi.org/10.1109/ICMLC.2005.1527946
R. Kamalian, Y. Zhang, and A.M. Agogino, "Microfabrication and Characterization of Evolutionary MEMS Resonators", Proceedings of the 2005 IEEE International Symposium on Micro-NanoMechatronics and Human Science, Nagoya, Japan, 2005, pp. 109-114. http://dx.doi.org/10.1109/MHS.2005.1589972
B. Knapp, "Fuzzy Sets and Pattern Recognition". Available: http://hci.sapp.org/lectures/knapp/fuzzy/fuzzy.pdf.
I. Ecemis, E. Bonabeau, and T. Ashburn, "Interactive Estimation of Agent-Based Financial Markets Models: Modularity and Learning", Proceedings of GECCO 2005, Genetic and Evolutionary Computation Conference, 2005, pp.1897-1904. http://dx.doi.org/10.1145/1068009.1068330
D. S. Todd and P. Sen, "Directed Multiple Objective Search of Design Spaces Using Genetic Algorithms and Neural Networks", Proceedings of GECCO 1999, Genetic and Evolutionary Computation Conference, 1999, pp. 1738-1743.
L. Zadeh, "Fuzzy Sets", Information and Control, Vol. 8, 1965.
S. Russell and P. Norvig, Artificial Intelligence, A Modern Approach. Prentice Hall, Upper Saddle River, NJ, 2001.
J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993.
Y. Freund and R. Schapire, "Experiments with a New Boosting Algorithm", Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148-156, 1996.
V. Vapnik, The Nature of Statistical Learning Theory. Springer, NY, 1995.
C. Cortes and V. Vapnik, "Support-Vector Networks". Machine Learning 20, pp. 273-297, 1995. http://dx.doi.org/10.1007/BF00994018
C. Chang and C. Lin, "LIBSVM: A Library for Support Vector Machines". Available at http://csie.ntu.edu.tw/~cjlin/libsvm.
J.-S. R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference Systems", IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, May 1993. http://dx.doi.org/10.1109/21.256541
R. Ranawana, V. Palade, "Multi Classifier Systems - A Review and Roadmap for Developers", to appear in March 2006 issue of the Journal of Hybrid Intelligent Systems, IOS Press Amsterdam.