When implementing a genetic algorithm, in order to develop a symbolic regression solution to our datasets, we had to make decisions concerning selection methods, bloat control and fitness evaluation.

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 ...

For more details on genetic programming and symbolic regression, see: Genetic programming: An introduction and survey of applications; Symbolic regression; Dependencies. The algorithm is written in ...

Learn how to use genetic programming to generate novel and interpretable symbolic expressions for AI problems, such as regression, classification, or clustering. Skip to main content LinkedIn Articles ...

A. Augusto, “symbolic regression via Genetic Program-ming,” VI Brazilian Symposium on Neural Network, pp. 173–178, 2000. T. Walter, “Recombination, selection, and the genetic construction of computer ...

Genetic programming (GP) represents a class of evolutionary algorithms that automates the creation of computer programmes to solve complex problems. Coupled with symbolic regression (SR), which ...