Abstract
Automated design of circuits is a vital task, which becomes more and more challenging due to the conflict of ever-growing scales and complexities of circuits and slow acquisition of relevant knowledge. Evolutionary design of circuit (EDC) combined with data mining is a promising way to solve the problem. To improve EDC in the aspects of efficiency, scalability and capability of optimization, a novel technique is developed. It features an adaptive multi-objective genetic algorithm and interactions between EDC and data mining. The proposed method is validated by the experiments on arithmetic circuits, showing many exciting results especially some novel knowledge discovered from the EDC data.
This work was supported by China Postdoctoral Science Foundation (No. 2005037783).
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References
Yao, X., Higuichi, T.: Promises and Challenges of Evolvable Hardware. IEEE Trans. On Systems Man and Cybernetics-Part C 1, 87–97 (1999)
Zhao, S.G.: Study of the Evolutionary Design Methods of Electronic Circuits. Ph.D. dissertation (in Chinese), Xidian University, China (2003)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Vassilev, V.K., Job, D., Miller, J.F.: Towards the Automatic Design of More Efficient Digital Circuits. In: Proceedings of EH 2000, pp. 151–160. IEEE, PaloAlto (2000)
Coello Coello, A.C., et al.: Use of Evolutionary Techniques to Automate the Design of Combinational Circuits. Inter. J. of Smart Engineering System Design 4, 299–314 (2000)
Zhao, S.G., Yang, W.H.: Intrinsic Hardware Evolution Based on a Prototype of Function Level FPGA. Chinese Journal of Computers 6, 666–669 (2002)
Shang, Y.C., Cai, X.M.: General Bionomics. Beijing University Press, Beijing (1992)
Gilbert, S.F.: Developmental Biology, 6th edn. Sinauer Associates Inc., Sunderland (2000)
Miller, J.F., Job, D., Vassilev, V.K.: Principles in the Evolutionary Design of Digital Circuits: Part I. J. of Genetic Programming and Evolvable Machines 1/2, 7–35 (2000)
Miller, J.F., Job, D., Vassilev, V.K.: Principles in the Evolutionary Design of Digital Circuits: Part II. J. of Genetic Programming and Evolvable Machines. 3, 259–288 (2000)
Zhao, S.G., Jiao, L.C., Zhao, J.: Multi-objective Evolutionary Design and Knowledge Discovery of Logic Circuits with an Improved Genetic Algorithm. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 273–278. Springer, Heidelberg (2005)
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Zhao, S., Zhao, M., Li, J., Wang, C. (2006). CBR-Based Knowledge Discovery on Results of Evolutionary Design of Logic Circuits. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_96
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DOI: https://doi.org/10.1007/11811305_96
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