Convergency of Genetic Regression In Data Mining Based On Gene Expression Programming and Optimized Solution
Changan Yuan, Chunming Tang, Wen Yu, Jiali Zuo, Jian Peng, Jing Hu · International Journal of Computers and Applications · 2006
This paper investigates the convergency of the probability of genetic regression in data mining based on Gene Expression Programming (GEP ) and the proposed optimized algorithm based on GEP-Minimized Residual Sum of Square Genetic Algorithm (MRSSGA). By extensive experiments on Genetic Programming (GP), GEP and MRSSGA show: (1) that all algorithms could find the target function from the data with low noise; (2) by comparing the convergency speeds, new algorithms in GEP are 20 times faster than GP and MRSSGA and 60 times faster than GP for simple data; (3) for very complex data with an unknown function type, GEP and MRSSGA are respectively 900 and 1800 times faster than GP at finding ideal functions; and (4) aimed at the actual data, the precision of models created by using genetic regression methods is much more exact than traditional methods.