Abstract
In the literature, evolutionary algorithms (EAs) are supposed to be efficient for designing large-scaled systems. This is true in the sense that EAs can provide high probability for obtaining the global optimal solutions. However, in most existing EAs, each individual corresponds directly to a system, and therefore the computational amount is so large that only small systems can be designed. This paper introduces the co-evolutionary algorithm (CEA) based on the concept of divide and conquer. The basic idea is to divide the system into many small homogeneous modules, define an individual as a module, find many good individuals using existing EAs, and put them together again to form the whole system. To make the study more concrete, we concentrate on the evolutionary learning of neural networks for pattern recognition. Experimental results are provided to show the procedure and the performance of the CEA.
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References
D. B. Fogel, Evolutionary Computation, IEEE Press, 1995.
J. R. Koza, Genetic Programming, Fourth Printing, The MIT Press, 1994.
R. Tanese, Distributed genetic algorithm for function optimization, Ph.D. dissertation. Department of Electrical Engineering and Computer Science, University of Michigan.
N. Hansen and A. Ostermeier, “Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation,” Proc. ICEC'96, pp. 312–317, Nagoya, May 1996.
E. Yourdon and L. L. Constantine, Structured Design, Prentice Hall, 1979.
M. Minsky, The Society of Mind, Simon and Schuster, 1986.
B. J. Cox, Object-oriented programming. An Evolutionary Approach, Addison-Wesley, 1986.
T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification,” IEEE Trans. on Information Theory, Vol. IT-13, No. 1, pp. 21–27, Jan. 1967.
Q. F. Zhao and T. Higuchi, “Evolutionary learning of nearest neighbor MLP,” IEEE Trans. on Neural Networks, Vol.7, No. 3, pp. 762–767, 1996.
T. Kohonen, “The self-organizing map,” Proc. IEEE, Vol. 78, No. 9, pp. 1464–1480, Sept. 1990.
G. A. Carpenter and S. Grossberg, “The ART of adaptive pattern recognition by a self-organizing neural network,” IEEE Computer, Vol. 21, No. 3, pp. 77–88, Mar. 1988.
S. Geva and J. Sitte, “Adaptive nearest neighbor pattern classification,” IEEE Trans. on Neural Networks, Vol. 2, No.2, pp. 318–322, Mar. 1991.
O. J. Murphy, “Nearest neighbor pattern classification perceptions,” Proc. IEEE, Vol. 78, No. 10, pp. 1595–1598, Oct. 1990.
N. K. Bose and A. K. Garga, “Neural network design using Voronoi diagrams,” IEEE Trans. on Neural Networks, Vol. 4, No. 5, pp. 778–787, Sept. 1993.
D. L. Reilly, L. N. Cooper and C. Elbaum, “A neural model for category learning,” Biol. Cybern. 45, pp. 35–41, 1982.
Q. F. Zhao, “Neural network learning based on co-evolution,” Proc. ICNN'96, 403–407, Washington, D.C., June 1996.
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© 1997 Springer-Verlag Berlin Heidelberg
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Zhao, Q. (1997). A study on co-evolutionary learning of neural networks. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028528
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DOI: https://doi.org/10.1007/BFb0028528
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