Multi-Space Competitive DGA for Model Selection and its Application to Localization of Multiple Signal Sources
Shudai Ishikawa*, Hideaki Misawa*, Ryosuke Kubota**,
Tatsuji Tokiwa*, Keiichi Horio*,***,
and Takeshi Yamakawa*,***
*Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology Kitakyushu, 2-4 Hibikino, Wakamatsu-ku, Kitakyushu 808-0196, Japan
**Department of Intelligent Systems Engineering, Ube National College of Technology, 2-14-1 Tokiwadai, Ube, Yamaguchi 755-8555, Japan
***Fuzzy Logic Systems Institute, 680-41 Kawazu, Iizuka, Fukuoka 820-0067, Japan
In this paper, a new optimization method, which is effective for the problems that the optimum solution should be searched in several solution spaces, is proposed. The proposed method is an extension of Distributed Genetic Algorithm (DGA), in which each subpopulation searches a solution in the corresponding solution space. Through the competition between the sub-populations, population sizes are adequately and gradually changed. By the change of the population size, the appropriate sub-population attracts many individuals. The changing population size yield the efficient search for the problems of searching for solutions in multiple spaces. In order to evaluate the proposed method, it is applied to a polynomial curve fitting and signal source localization, in which the number of sources is preliminarily unknown. Simulation results show the effectiveness of the proposed method.
Tatsuji Tokiwa, Keiichi Horio, and
and Takeshi Yamakawa, “Multi-Space Competitive DGA for Model Selection and its Application to Localization of Multiple Signal Sources,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.9, pp. 1320-1328, 2011.
-  J. H. Holland, “Outline for a Logical Theory of Adaptive Systems,” J. of the Association for Computing Machinery, Vol.3, pp. 297-314, 1962.
-  S. Tsutsui and Y. Fujimoto, “The fGA: Forking Genetic Algorithm with Blocking and Shrinking Modes,” Proc. 5th ICGA, pp. 206-213, 1993.
-  D. Whitley, “The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best,” Proc. 3rd ICGA, pp. 116-121, 1989.
-  H. Takagi, “Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation,” Proc. of the IEEE, Vol.89, No.9, pp. 1275-1296, 2001.
-  R. Tanese, “Parallel Genetic Algorithm for a Hypercube,” Proc. of the Int. Conf. on Genetic Algorithms, pp. 177-183, 1987.
-  R. Tanese, “Distributed Genetic Algorithms,” Proc. of the Int. Conf. on Genetic Algorithms, pp. 434-439, 1989.
-  M. Kim, V. Aggarwal, U.-M. O’ Reilly, and M. Medard, “A Doubly Distributed Genetic Algorithm for Network Coding,” Proc. ACM Genetic and Evolutionary Computation Conference (GECCO), 2007.
-  A. Peregrin and M. A. Rodriguez, “Multiple Source Estimation Method Combined with Genetic Algorithm and Simulated Annealing,” Eighth International Conference on Hybrid Intelligent Systems, pp. 531-536, 2008.
-  S. Baillet, J. C. Mosher, and R. M. Leahy, “Electromagnetic Brain Mapping,” IEEE Signal Processing Magazine, Vol.18, No.6, pp. 14-30, 2001.
-  C. Michel, M. Murray, G. Lantz, S. Gonzalez Andino, L. Spinelli, and R. Grave de Peralta Menendez, “EEG source imaging,” Clinical Neurophysiology, Vol.115, No.10, pp. 2195-2222, 2004.
-  D. McNay, E. Michielssen, R. L. Rogers, S. A. Taylor, M. Akhtari, and W. W. Sutherling, “Multiple source localization using genetic algorithms,” J. of Neuroscience Methods, Vol.64, Issue 2, pp. 163-172, 1996.
-  T. Nagano, Y. Ohno, N. Uesugi, H. Ikeda, and A. Ishiyama, “Multisource localization by Genetic Algorithms using MEG,” IEEE Trans. on Magnetics, Vol.34, No.5, 1998.
-  Y. Ono, A. Ishiyama, and N. Kasai, “Multiple Source Estimation Method Combined with Genetic Algorithm and Simulated Annealing,” Trans. of the Institute of Electrical Engineers of Japan, Vol.122-A, pp. 93-99, 2002 (in Japanese).
-  C. M. Bishop, “Pattern Recognition and Machine Learning,” Springer, 2006.
-  L. J. Eshelman and J. D. Schaffer, “Real Coded Genetic Algorithms and Interval-Schemata,” Foundations of Genetic Algorithms 2, Morgan Kaufman Publishers, San Mateo, pp. 187-202, 1993.
-  H. Akaike, “Information theory and an extension of the maximum likefood principle,” Second Int. Symposium on Information Theory, Akademiai Kiado, pp. 267-281, 1973.
-  Z. Zhang, “A fast method to compute surface potentials generated by dipole within multilayer anisotropic spheres,” Phys. Med. Biol., Vol.40, pp. 335-349, 1995.
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