조선분야의 축적된 데이터 활용을 위한 유전적프로그래밍에서의 선형(Linear) 모델 개발

초록

Until now, Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts’ experience and know-how in its own. It is very useful to extract knowledge or information from the accumulated existing data by using datamining technique. This paper treats a machine learning method based on genetic programming (GP), which can be one of the components to realize datamining. The paper deals with linear models of GP for the regression or approximation problem when given learning samples are not sufficient. The linear model, which is a function of unknown parameters, is built through extracting all possible base functions from the standard GP tree by utilizing the symbolic processing algorithm. In addition to a standard linear model consisting of mathematic functions, one variant form of a linear model, which can be built using low order Taylor series and can be converted into the standard form of a polynomial, is considered in this paper.