Adaptive Random Search with Intensification and Diversification Combined with Genetic Algorithm
Dongkyu Sohn, Hiroyuki Hatakeyama, Shingo Mabu,
Kotaro Hirasawa, and Jinglu Hu
Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan
A novel optimization method named RasID-GA (an abbreviation of Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm) is proposed in order to enhance the searching ability of conventional RasID, which is a kind of Random Search with Intensification and Diversification. In this paper, the timing of switching from RasID to GA, or from GA to RasID is also studied. RasID-GA is compared with parallel RasIDs and GA using 23 different objective functions, and it turns out that RasID-GA performs well compared with other methods.
-  K. Hirasawa, H. Miyazaki, and J. Hu, “Enhancement of RasID and Its Evaluation,” T.SICE, Vol.38, No.9, pp. 775-783, 2002.
-  K. Hirasawa, K. Togo, J. Hu, M. Ohbayashi, and J. Murata, “A New Adaptive Random Search Method in Neural Networks –RasID–,” T.SICE, Vol.34, No.8, pp. 1088-1096, 1998.
-  J. Hu, K. Hirasawa, and J. Murata, “RasID-Random Search for Neural Network Training,” Journal of Advanced Computational Intelligence, Vol.2, No.4, pp. 134-141, 1998.
-  J. Hu and K. Hirasawa, “Adaptive random search approach to identification of neural network model,” Proceedings of the 31st ISCIE international symposium on stochastic systems theory and its applications, Yokohama, pp. 73-78, Nov. 11-12, 1999.
-  D. Sohn, K. Hirasawa, and J. Hu, “Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm,” Congress on Evolutionary Computation 2005 (CEC2005), pp. 1462-1469, 2005.
-  X. Yao, Y. Liu, and G. Lin, “Evolutionary Programming Made Faster,” IEEE Tran. on Evolutionary Computation, Vol.3, No.2, pp. 82-102, 1999.
-  Y. W. Leung and Y. Wang, “An orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization,” IEEE Tran. on Evolutionary Computation, Vol.5, No.1, pp. 41-53, 2001.
-  X. Yao and Y. Liu, “Fast evolution strategies,” in Evolutionary Programming VI, P. J. Angeline, R. Reynolds, J. McDonnell, and R. Eberhart (Eds.), Berlin, Germany: Springer-Verlag, pp. 151-161, 1977.
-  P. A. Moscato, “On evolution, search, optimization, genetic algorithms and maritial arts: Toward memetic algorithms,” Caltech Concurrent Computation program, California Institute of Technology, Pasadena, Tech. Rep. 790, 1989.
-  D. Molina, F. Herrera, and M. Lozano, “Adaptive Local Search Parameters for Real-Coded Memetic Algorithms,” Congress on Evolutionary Computation 2005 (CEC2005), pp. 888-895, 2005.
-  J. Matyas, “Random optimization,” Automation and Remote Control, Vol.26, pp. 244-251, 1965.
-  F. J. Solis and J. B. Wets, “Minimization by random search techniques,” Mathematics of Operations Research, Vol.6, pp. 19-30, 1981.
-  A. Torn and A. Zilinskas, “Global optimization,” in Lecture Notes in Computer Science, 350, Berlin, Germany, Springer-Verlag, 1989.
-  H. P. Schwefel, “Evolution and Optimum Seeking,” New York, Wiley, 1995.
-  J. Holland, “Adaptation in Natural and Artificial System,” Ann Arbor, MIT University of Michigan Press, 1975.
-  J. E. Baker, “Adaptive selection methods for genetic algorithms,” in Proc. of the first International Conference on Genetic Algorithms, pp. 101-111, 1985.
-  D. E. Goldberg, B. Korb, and K. Deb, “Messy genetic algorithm: Motivation analysis, and first results,” Complex Systems, Vol.3, pp. 493-530, 1989.
-  S. Tsutsui and D. E. Goldberg, “Simplex Crossover and Linkage Identification: Single-Stage Evolution VS. Multi-Stage Evolution,” Proceedings of the 2002 Congress on Evolutionary Computation (CEC’02), pp. 974-979, 2002.
-  S. Tsutsui, M. Yamamura, and T. Higuchi, “Multi-parent Recombination with Simplex Crossover in Real Coded Genetic Algorithms,” Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECCO-99), pp. 657-664, 1999.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.