Model-driven regularization approach to straight line program genetic programming

作者：

Highlights：

• A regularization method for linear genetic programming is proposed.

• Straight line programs with transcendental elementary functions are used.

• A sharp bound for the Vapnik–Chervonenkis dimension of programs is encountered.

• Our approach is empirically better than other statistical regularization methods.

摘要

•A regularization method for linear genetic programming is proposed.•Straight line programs with transcendental elementary functions are used.•A sharp bound for the Vapnik–Chervonenkis dimension of programs is encountered.•Our approach is empirically better than other statistical regularization methods.

论文关键词：Genetic programming,Straight line program,Pfaffian operator,Symbolic regression

论文评审过程：Received 6 November 2015, Revised 5 February 2016, Accepted 3 March 2016, Available online 19 March 2016, Version of Record 5 April 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.03.003