Highly Accurate Symbolic Regression with Noisy Training Data 115
Korns M (2012). A baseline symbolic regression algorithm. In: Genetic programming theory and
practice X. Springer, New York, Kaufmann Publishers, San Francisco, CA
Korns M (2013). Extreme accuracy in symbolic regression. In: Genetic programming theory and
practice XI. Springer, New York, Kaufmann Publishers, San Francisco, CA
Korns M (2014). Extremely accurate symbolic regression for large feature problems. In: Genetic
programming theory and practice XII. Springer, New York, Kaufmann Publishers, San
Francisco, CA
Kotanchek M, Smits G, Vladislavleva E (2008) Trustable symbolic regression models: using
ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models. In:
Genetic programming theory and practice V. Springer, New York
Koza JR (1992) Genetic programming: on the programming of computers by means of natural
selection. The MIT Press, Cambridge, MA
Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. The MIT
Press, Cambridge, MA
Koza JR, Bennett FH III, Andre D, Keane MA (1999) Genetic programming III: Darwinian
invention and problem solving. Morgan Kaufmann, San Francisco, CA
McConaghy T (2011) FFX: fast, Scalable, deterministic symbolic regression technology. In:
Genetic programming theory and practice IX. Springer, New York
Nelder JA, Wedderburn RW (1972) Generalized linear models. J Roy Stat Soc Ser A Gen 135:
370–384
Poli R, McPhee N, Vanneshi L (2009) Analysis of the effects of elitism on bloat in linear and
tree-based genetic programming. In: Genetic programming theory and practice VI. Springer,
New York
Schmidt M, Lipson H (2010) Age-fitness pareto optimization. In: Genetic programming theory and
practice VI. Springer, New York
Smits G, Kotanchek M (2005). Pareto-front exploitation in symbolic regression. In: Genetic
programming theory and practice II. Springer, New York