Fitting Optimal Piecewise Linear Functions Using Genetic Algorithms

摘要

In this paper, we examine the use of genetic algorithms to fit piecewise linearfunctions to data in R2. The number of pieces, the location of the knots, and the underlying distribution of the data are assumed to be unknown. We discuss existing methods which attempt to solve this problem and introduce a new method which employs genetic algorithms to optimize the number and location of the linear pieces. We prove theoretically that our method provides near-optimal functions and present the results of extensive experiments which demonstrate that the proposed method provides better results than existing spline based methods. We conclude that our method represents a valuable tool for fitting both robust and non-robust piecewise linear functions.