Abstract
In this paper, we present a genetic algorithm (GA) based on tournament selection (TS) and deterministic mutation (DM) to evolve neural network systems. We use population diversity to determine the mutation probability for sustaining the convergence capacity and preventing local optimum problem of GA. In addition, we consider population that have a worst fitness and best fitness value for tournament selection to fast convergence. Experimental results with mathematical problems and pattern recognition problem show that the proposed method enhance the convergence capacity about 34.5% and reduce computation power about 40% compared with the conventional method.
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References
Hicklin, J., Demuth, H.: Modeling Neural Networks on the MPP. In: Proceeding of 2nd Symposium on the Frontiers of Massively Parallel Computation, pp. 39–42 (1988)
Ho, C.W., Lee, K.H., Leung, K.S.: A Genetic Algorithm Based on Mutation and Crossover with Adaptive Probabilities. In: Proceeding of the 1999 Congress on Evolutionary Computation, pp. 768–775 (1999)
Sunghoon, J.: Queen-bee Evolution for Genetic Algorithms. Electron. Lett. 39(6), 575–576 (2003)
Kim, J.J., Choi, Y.H., Lee, C.H., Chung, D.J.: Implementation of a High-Performance Genetic Algorithm Processor for Hardware Optimization. IEICE Trans. Electron (1), 195–203 (2002)
Hesser, M.R.: Towords an Optimal Mutation Probability for Genetic Algorithms. In: Proceedings of the first Conference on Parallel Problem Solving from Nature, pp. 23–32 (1990)
Gong, D., Pan, F., Sun, X.: Research on a Novel Adaptive Genetic Algorithm. In: Proceeding of the 2002 IEEE International Symposium on Industrial Electronics, pp. 357–359 (2002)
Yentis, R., Zaghloul, M.E.: VLSl Implementation of Focally Connected Neural Networks for Solving Partial Differential Equations. IEEE Trans. Circnits Syst. I, Fundaln. Theory Appl. 43(8), 687–690 (1996)
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, DS., Kim, HS., Chung, DJ. (2006). A Genetic Algorithm with Modified Tournament Selection and Efficient Deterministic Mutation for Evolving Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_106
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DOI: https://doi.org/10.1007/11759966_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
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