article · 2002

Symbolic regression via genetic programming

Cited by 109Abstract and details only

Abstract

Presents an implementation of symbolic regression which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled languages are not usually the most efficient ones. The present approach employs a simple representation for tree-like structures by making use of Read's linear code, leading to more simplicity and better performance when compared with traditional GP implementations. Creation, crossover and mutation of individuals are formalized. An extension allowing for the creation of random coefficients is presented. The efficiency of the proposed implementation was confirmed in computational experiments which are summarized in the paper.

Cite this paper

APA 7

Augusto, D. A., & Barbosa, H. J. C. (2002). Symbolic regression via genetic programming. https://doi.org/10.1109/sbrn.2000.889734

MLA 9

Augusto, Douglas A., and Hélio J. C. Barbosa. “Symbolic regression via genetic programming.” 2002, pp. 173–78, https://doi.org/10.1109/sbrn.2000.889734.

Chicago

Augusto, Douglas A., and Hélio J. C. Barbosa. 2002. “Symbolic regression via genetic programming.” https://doi.org/10.1109/sbrn.2000.889734.

Harvard

Augusto, D.A. and Barbosa, H.J.C. (2002) ‘Symbolic regression via genetic programming’. Available at: https://doi.org/10.1109/sbrn.2000.889734.

BibTeX

@article{augusto2002symbolic,
  title = {{Symbolic regression via genetic programming}},
  author = {Augusto, Douglas A. and Barbosa, Hélio J. C.},
  pages = {173--178},
  year = {2002},
  doi = {10.1109/sbrn.2000.889734}
}

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