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Development of Data Miner for the Ship Design Based on Polynomial Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4304))

Abstract

Engineering data contains the experiences and know-how of experts. Data mining technique is useful to extract knowledge or information from the accumulated existing data. This paper deals with generating optimal polynomials using genetic programming (GP) as the module of Data Miner. The Data Miner for the ship design based on polynomial genetic programming is presented.

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References

  1. Simpson, T.W., Allen, J.K., Mistree, F.: Spatial Correlation and Metamodels for Global Approximation in Structural Design Optimization. In: Proc. of DETC 1998, ASME (1998)

    Google Scholar 

  2. Malik, Z., Su, H., Nelder, J.: Informative Experimental Design for Electronic Circuits. Quality and Reliability Engineering 14, 177–188 (1986)

    Google Scholar 

  3. Alotto, P., Gaggero, M., Molinari, G., Nervi, M.: A Design of Experiment and Statistical Approach to Enhance the Generalized Response Surface Method in the Optimization of Multi-Minimas. IEEE Transactions on Magnetics 33(2), 1896–1899 (1997)

    Article  Google Scholar 

  4. Ishikawa, T., Matsunami, M.: An Optimization Method Based on Radial Basis Function. IEEE Transactions on Magnetics 33(2/II), 1868–1871 (1997)

    Article  Google Scholar 

  5. Ott, R.L.: An Introduction to Statistical Methods and Data Analysis, Wadsworth Inc. (1993)

    Google Scholar 

  6. Myers, R.H., Montgomery, D.C.: Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons, Inc., Chichester (1995)

    MATH  Google Scholar 

  7. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  8. Yeun, Y.S., Lee, K.H., Yang, Y.S.: Function Approximations by Coupling Neural Networks and Genetic Programming Trees with Oblique Design Trees. AI in Engineering 13(3) (1999)

    Google Scholar 

  9. Lee, K.H., et al.: Data Analysis and Utilization Method Based on Genetic Programming in Ship Design. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3981, Springer, Heidelberg (2006)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Lee, K.H., Oh, J., Park, J.H. (2006). Development of Data Miner for the Ship Design Based on Polynomial Genetic Programming. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_108

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  • DOI: https://doi.org/10.1007/11941439_108

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49787-5

  • Online ISBN: 978-3-540-49788-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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