Abstract
As an important branch of evolvable hardware, evolutionary design of circuit (EDC) is a promising way to realize automated design of complex electronic circuits. To improve EDC in efficiency, scalability and capability of optimization, a novel technique was developed. It features an adaptive multi-objective genetic algorithm and interactions between EDC and data mining. It was validated by the experiments on arithmetic circuits, showing some exciting results. Some circuits evolved are the best ones ever reported in terms of gate count and operating speed. Moreover, some novel knowledge, e.g., efficient and scalable design formulae and generalized transform rules have been discovered by mining the data and results of EDC, which are easy to verify but difficult to dig out by human experts with existing knowledge.
This work was partially supported by National Natural Science Foundation of China (under grants No. 60133010 and No. 60374063) and China Postdoctoral Science Foundation.
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Zhao, S., Zhao, M., Zhao, J., Jiao, L. (2006). Towards Automated Design of Large-Scale Circuits by Combining Evolutionary Design with Data Mining. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_100
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DOI: https://doi.org/10.1007/11731139_100
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