Curve and surface fitting with splines
Curve and surface fitting with splines
Pattern classification with genetic algorithms
Pattern Recognition Letters - Special issue on genetic algorithms
Genetic algorithms and their statistical applications: an introduction
Computational Statistics & Data Analysis
Pattern classification using genetic algorithm: determination of H
Pattern Recognition Letters
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Structural break estimation of noisy sinusoidal signals
Signal Processing
Matrix-based genetic algorithm for computing the minimum volume ellipsoid
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Hardware based genetic evolution of self-adaptive arbitrary response FIR filters
Applied Soft Computing
Genetic design of feature spaces for pattern classifiers
Artificial Intelligence in Medicine
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Constructing a model for data in ${\cal R}^{2}$ 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 ${\cal R}^{2}$. 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.