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Automated Design and Knowledge Discovery of Logic Circuits Using a Multi-objective Adaptive GA

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AI 2005: Advances in Artificial Intelligence (AI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3809))

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Abstract

Both automated design and knowledge discovery of electronic circuits are challenging tasks for artificial intelligence. A genetic algorithm (GA) based approach to them was proposed in this paper, which features an array-based encoding scheme, a multi-objective evaluation mechanism and an adaptation strategy for GA parameters. It was validated by the experiments on arithmetic circuits of gradually increasing scales, which evolved multi-objective optimized circuits and revealed some novel and generalized principles.

This work was partially supported by National Natural Science Foundation of China under grant 60374063, and granted financial support from China Postdoctoral Science Foundation.

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, 710071, P. R. China

    Shuguang Zhao, Licheng Jiao & Min Tang

Authors
  1. Shuguang Zhao
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  2. Licheng Jiao
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  3. Min Tang
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Editors and Affiliations

  1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

    Shichao Zhang

  2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

    Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhao, S., Jiao, L., Tang, M. (2005). Automated Design and Knowledge Discovery of Logic Circuits Using a Multi-objective Adaptive GA. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_128

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  • DOI: https://doi.org/10.1007/11589990_128

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30462-3

  • Online ISBN: 978-3-540-31652-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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