Title :
Notice of Retraction
Feature fitness evaluation for symbolic regression via genetic programming
Author :
Qiang Lu ; Bin Wang
Author_Institution :
Dept. of Comput. Sci. & Technol., China Univ. of Pet., Beijing, China
Abstract :
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, feature fitness evaluation method is proposed for accelerating the speed of evolution in symbolic regression. Through analyzing the feature of curve or surface which train data represents, vertex and inflection points are extracted from the train data. According to the feature data and diversity of population, the test data for evolution of genetic programming (GP) are generated dynamically. The method was implemented by using GP and genetic expression programming(GEP). Results show that the method in GP, compared with classic GP and GEP, has benefits about efficient of computation, regression performance and avoiding premature convergence.
Keywords :
genetic algorithms; regression analysis; GEP; data representation; feature fitness evaluation; genetic expression programming; inflection points; symbolic regression; vertex points; Convergence; Encoding; Evolutionary computation; Feature extraction; Genetic programming; Programming; Strontium; Symbol regression; fitness evaluaton; genetic programming;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022150
