Fitting optimal piecewise linear functions using genetic algorithms

Jennifer Pittman, C. A. Murthy

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Constructing a model for data in R2 is a common problem in many scientific fields, including pattern recognition, computer vision, and applied mathematics. Often little is known about the process which generated the data or its statistical properties. For example, in fitting a piecewise linear model, the number of pieces, as well as the knot locations, may be unknown. Hence, the method used to build the statistical model should have few assumptions, yet, still provide a model that is optimal in some sense. Such methods can be designed through the use of genetic algorithms. In this paper, we examine the use of genetic algorithms to fit piecewise linear functions 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 pieces. Experimental results are presented which demonstrate the performance of our method and compare it to the performance of several existing methods. We conclude that our method represents a valuable tool for fitting both robust and nonrobust piecewise linear functions.

Original languageEnglish (US)
Pages (from-to)701-718
Number of pages18
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number7
StatePublished - Jul 2000
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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