Abstract
An optimal trade-off between exploration and exploitation properties of genetic algorithm is very important in optimization process. Due value steering of these two factors we can prevent a premature convergence of the algorithm. Therefore, better results can be obtained during optimization process with the use of genetic algorithm. In this paper the type-2 fuzzy logic control of trade-off between exploration and exploitation properties of genetic algorithm is presented. Our novel selection method (with application of type-2 fuzzy logic to steering of key parameter in this selection method) is based on previously elaborated mix selection method. In proposed method two factors are taken into consideration: the first is a number of generations of genetic algorithm, and second is a population diversity. Due to these two factors, we can control the trade-off between global and local search of solution space; also due to the type-2 fuzzy control the proposed method is more ”immune” in falling into the trap of local extremum. The results obtained using proposed method (during optimization of test functions chosen from literature) are compared with the results obtained using other selection methods. Also, a statistically importance of obtained results is checked using statistical t-Student test. In almost all cases, the results obtained using proposed selection method are statistically important and better than the results obtained using other selection techniques.
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References
Bäck, T.: Selective Pressure in Evolutionary Algorithms: A Characterization of Selection Mechanisms. In: Proc. 1st IEEE Conf. on Evolutionary Computing, pp. 57–62 (1994)
Liu, S.-H., Mernik, M., Bryant, B.R.: Entropy-Driven Parameter Control for Evolutionary Algorithms. Informatica 31, 41–50 (2007)
Motoki, T.: Calculating the expected loss of diversity of selection schemes. Evolutionary Computation 10(4), 397–422 (2002)
Winkler, S., Affenzeller, M., Wagner, S.: Offspring selection and its effects on genetic propagation in genetic programming based system identification. Cybernetics and Systems 2, 549–554 (2008)
Xie, H., Zhang, M.: Tuning Selection Pressure in Tournament Selection, Technical Report Series, School of Engineering and Computer Science, Victoria University of Wellington, New Zealand (2009)
Jong, K.D.: Parameter setting in eas: a 30 year perspective. In: Parameter Setting in Evolutionary Algorithms, pp. 1–18. Springer, Heidelberg (2007)
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer, Heidelberg (1992)
Zen, S., Zhou Yang, C.T.: Comparison of steady state and elitist selection genetic algorithms. In: Proc. of 2004 Int. Conf. on Intelligent Mechatronics and Automation, pp. 495–499 (2004), doi:10.1109/ICIMA.2004.1384245
Takaaki, N., Takahiko, K., Keiichiro, Y.: Deterministic Genetic Algorithm. Papers of Technical Meeting on Industrial Instrumentation and Control, IEE Japan, pp. 33–36 (2003)
Blickle, T., Thiele, L.: A Comparison of Selection Schemes used in Genetic Algorithms, Computer Engineering and Communication Networks Lab, Swiss Federal Institute of Technology, TIK Report, No. 11, Edition 2 (December 1995)
Muhlenbein, H., Schlierkamp-voosen, D.: Predictive Models for the Breeder Genetic Algorithm. Evolutionary Computation 1(1), 25–49 (1993)
Słowik, A., Białko, M.: Modified Version of Roulette Selection for Evolution Algorithms – The Fan Selection. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 474–479. Springer, Heidelberg (2004), doi:10.1007/978-3-540-24844-6_70
Słowik, A.: Steering of Balance between Exploration and Exploitation Properties of Evolutionary Algorithms - Mix Selection. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6114, pp. 213–220. Springer, Heidelberg (2010), doi:10.1007/978-3-642-13232-2_26
Castillo, O., Cazarez, N., Rico, D.: Intelligent Control of Dynamic Systems Using Type-2 Fuzzy Logic and Stability Issues. International Mathematical Forum 1(28), 1371–1382 (2006)
Klir, G.J., Yuan, B.: Fuzzy sets and fuzzy logic: theory and applications. Prentice-Hall, Upper Saddle River (1995)
Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic: Introduction and new directions. Prentice Hall, USA (2000)
Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 Fuzzy Logic Systems. IEEE Transactions of Fuzzy Systems 7(6), 643–658 (1999), doi:10.1109/91.811231
Xue, F., Sanderson, A.C., Bonissone, P., Graves, R.J.: Fuzzy Logic Controlled Multi-Objective Differential Evolution. In: The 14th IEEE International Conference on Fuzzy Systems, pp. 720–725 (2005), doi:10.1109/FUZZY.2005.1452483
Karnik, N.N., Mendel, J.M.: Centroid of a type-2 fuzzy set. Information Sciences 132, 195–220 (2001), doi:10.1016/S0020-0255(01)00069-X
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Slowik, A. (2012). Type-2 Fuzzy Logic Control of Trade-off between Exploration and Exploitation Properties of Genetic Algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_43
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DOI: https://doi.org/10.1007/978-3-642-29353-5_43
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