A hierarchical genetic algorithm approach for curve fitting with B-splines

C. H. Garcia-Capulin, F. J. Cuevas, G. Trejo-Caballero, H. Rostro-Gonzalez

Research output: Indexed journal article Articlepeer-review

20 Citations (Scopus)

Abstract

Automatic curve fitting using splines has been widely used in data analysis and engineering applications. An important issue associated with data fitting by splines is the adequate selection of the number and location of the knots, as well as the calculation of the spline coefficients. Typically, these parameters are estimated separately with the aim of solving this non-linear problem. In this paper, we use a hierarchical genetic algorithm to tackle the B-spline curve fitting problem. The proposed approach is based on a novel hierarchical gene structure for the chromosomal representation, which allows us to determine the number and location of the knots, and the B-spline coefficients automatically and simultaneously. Our approach is able to find optimal solutions with the fewest parameters within the B-spline basis functions. The method is fully based on genetic algorithms and does not require subjective parameters like smooth factor or knot locations to perform the solution. In order to validate the efficacy of the proposed approach, simulation results from several tests on smooth functions and comparison with a successful method from the literature have been included.

Original languageEnglish
Pages (from-to)151-166
Number of pages16
JournalGenetic Programming and Evolvable Machines
Volume16
Issue number2
DOIs
Publication statusPublished - 10 Apr 2015
Externally publishedYes

Keywords

  • B-splines
  • Curve fitting
  • Genetic algorithm
  • Regression

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