Auto-Programming for Numerical Data Based on Remnant-Standard-Deviation-Guided Gene Expression Programming
Tao Zeng, Yintian Liu, Xirong Ma, Xiaoyuan Bao, Jiangtao Qiu, Lixin Zhan · 2009
Automatically numerical data modeling and computer code generation is significant for data mining, data reverse engineering, engineering applications, etc. On auto-programming for numerical data, a new approach, remnant-standard-deviation-guided gene expression programming (RD-GEP), was proposed. New individual structure, the K-expression to reverse Polish notation code generation without expression tree construction algorithm (K2RPN), and remnant-standard-deviation based fitness evaluation method in RD-GEP were presented and studied. New individual structure makes easy to I/O or storage the candidate solution. New decoding algorithm with linear-time complexity can simplify system operation and unify I/O format. New evaluation mechanism can reduce hypothesis solution space to improve system performance and precision. Feasibility and usability of RD-GEP were verified on various synthetic data sets and real ¿Fishcatch¿ data set. Experimental results showed RD-GEP is good at automatically modeling numerical data and generating reverse Polish notation for target model.