- author = "Joseph L. Awange and Bela Palancz",
- title = "Symbolic Regression",
- booktitle = "Geospatial Algebraic Computations: Theory and Applications",
- publisher = "Springer",
- year = "2016",
- chapter = "11",
- pages = "203--216",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-319-25465-4",
-
DOI = "
doi:10.1007/978-3-319-25465-4_11",
- abstract = "Symbolic regression (SR) is the process of determining the symbolic function, which describes a data set-effectively developing an analytic model, which summarizes the data and is useful for predicting response behaviours as well as facilitating human insight and understanding. The symbolic regression approach adopted herein is based upon genetic programming wherein a population of functions are allowed to breed and mutate with the genetic propagation into subsequent generations based upon a survival-of-the-fittest criteria. Amazingly, this works and, although computationally intensive, summary solutions may be reasonably discovered using current laptop and desktop computers.",
-
notes = "Author Affiliations
Curtin University, Perth, West Australia, Australia
Budapest University of Technology and Economics, Budapest, Hungary",
