Abstract
The technology of electronic design automation (EDA) has improved the efficiency of design process, however, designer is still required much special knowledge of circuit. During the past decade, using genetic algorithm (GA) to design circuit had attracted many experts and scholars. However, too much more attention was focus on a circuit’s function and many other factors had been neglected which caused the circuit had little applicability. This paper proposes an automated design approach for analog circuit based on a multi-objective adaptive GA. The multi-objective fitness evaluation method, which can dynamic adjust parameter, is selected. And a parallel evolution strategy which separates evolution of circuit structure and element value is adopted but also organically combined them by weight vectors. The experimental results indicate that this approach obviously be able to improve the evolution efficiency and could generate numbers of suitable circuits.
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Koza, J.R., Bennett, F.H., Andre, M.A., et al.: Automated synthesis of analog electrical circuits by means of genetic programming. IEEE Trans. on Evolutionary Computation 1(2), 109–128 (1997)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
de Garis, H.: Evolvable hardware: Genetic programming of a darwin machine. In: Proceeding of Artificial Neural Nets ad Genetic Algorithms, pp. 441–449. Springer, Wien (1993)
Cantu-Paz, E.: A survey of parallel genetic algorithms. Calculateurs parallels 10(2), 141–171 (1998)
Iwata, M.: A pattern recognition system using evolvable hardware. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 761–770. Springer, Heidelberg (1996)
de Garis, H.: An artificial brain: Atr’s cam-brain project aims to build/evolve an artificial brain with a million neural net modules inside a trillion cell cellular automata machine. New Generation Computing 12(2), 215–221 (1994)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
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Xia, X., Li, Y., Ying, W., Chen, L. (2007). Automated Design Approach for Analog Circuit Using Genetic Algorithm. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72590-9_168
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DOI: https://doi.org/10.1007/978-3-540-72590-9_168
Publisher Name: Springer, Berlin, Heidelberg
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