Enhanced Genetic Programming Approach for a Ship Design

  • Lee, Kyung-Ho (Department of Naval Architecture and Ocean Engineering, Inha University) ;
  • Han, Young-Soo (Department of Naval Architecture, Graduate School of Inha University) ;
  • Lee, Jae-Joon (Department of Naval Architecture, Graduate School of Inha University)
  • Published : 2007.12.31

Abstract

Recently the importance of the utilization of engineering data is gradually increasing. 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 Mining system. Low order Taylor series are used to approximate the polynomial easily as a nonlinear function to fit the accumulated data. The overfitting problem is unavoidable because in real applications, the size of learning samples is minimal. This problem can be handled with the extended data set and function node stabilization method. The Data Mining system for the ship design based on polynomial genetic programming is presented.

Keywords

References

  1. Alotto, P., M. Gaggero, G. Molinari and M. Nervi. 1997. A Design of Experiment and Statistical Approach to Enhance the Generalized Response Surface Method in the Optimization ofMulti-Minimas, IEEE Transactions on Magnetics, 33, 2, 1896-1899 https://doi.org/10.1109/20.582657
  2. Ishikawa, T. and M. Matsunami. 1997. An Optimization Method Based on Radial Basis Function, IEEE Transactions on Magnetics, 33, 2/11, 1868-1871 https://doi.org/10.1109/20.582647
  3. Koza, J.R 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press
  4. Lee, KH., Y.S. yeun, J.H. Lee and J. Oh. 2006. Data Analysis and Utilization Method Based on Genetic Programming in Ship Design, Lecture Notes in Computer Science, 3981
  5. Malik, Z., H. Su and J. NeIder. 1986. Informative Experimental Design for Electronic Circuits, Quality and Reliability Engineering, 14, 177-188
  6. Myers, R.H. and D.C. Montgomery. 1995. Response Surface Methodology: Process and Product Optimization Using Designed Experiments, John Wiley & Sons, Inc
  7. Ott, RL. 1993. An Introduction to Statistical Methods and Data Analysis, Wadsworth Inc
  8. Simpson, T.W., J.K, Allen and F. Mistree. 1998. Spatial Correlation and Metamodels for Global Approximation in Structural Design Optimization, Proc. of DETC98, ASME
  9. Yeun, Y.S., K.H. Lee and Y.S. Yang. 1999. Function Approximations by Coupling Neural Networks and Genetic Programming Trees with Oblique Design Trees, AI in Engineering, 13, 3