Genetic Programming for Symbolic Regression

@inproceedings{Zhang2014GeneticPF,
  title={Genetic Programming for Symbolic Regression},
  author={Chi Zhang},
  year={2014}
}
Genetic programming (GP) is a supervised learning method motivated by an analogy to biological evolution. GP creates successor hypotheses by repeatedly mutating and crossovering parts of the current best hypotheses, with expectation to find a good solution in the evolution process. In this report, the task to be performed was a symbolic regression problem… CONTINUE READING