%0 Conference Paper %1 Zhang:gecco06lbp %A Zhang, Qiongyun %A Zhou, Chi %A Xiao, Weimin %A Nelson, Peter C. %A Li, Xin %B Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006) %C Seattle, WA, USA %D 2006 %E Grahl, Jörn %K DE algorithms, expression gene genetic programming, %T Using Differential Evolution for GEP Constant Creation %U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp130.pdf %X Gene Expression Programming (GEP) is a new evolutionary algorithm that incorporates both the idea of simple, linear chromosomes of fixed length used in Genetic Algorithms (GAs) and the structure of different sizes and shapes used in Genetic Programming (GP). As with other genetic programming algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this paper, we describe a new approach of constant generation using Differential Evolution (DE), which is a simple real-valued GA that has proven to be robust and efficient on parameter optimisation problems. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variants.

@inproceedings{Zhang:gecco06lbp, abstract = {Gene Expression Programming (GEP) is a new evolutionary algorithm that incorporates both the idea of simple, linear chromosomes of fixed length used in Genetic Algorithms (GAs) and the structure of different sizes and shapes used in Genetic Programming (GP). As with other genetic programming algorithms, GEP has difficulty finding appropriate numeric constants for terminal nodes in the expression trees. In this paper, we describe a new approach of constant generation using Differential Evolution (DE), which is a simple real-valued GA that has proven to be robust and efficient on parameter optimisation problems. Our experimental results on two symbolic regression problems show that the approach significantly improves the performance of the GEP algorithm. The proposed approach can be easily extended to other Genetic Programming variants.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Seattle, WA, USA}, author = {Zhang, Qiongyun and Zhou, Chi and Xiao, Weimin and Nelson, Peter C. and Li, Xin}, biburl = {https://www.bibsonomy.org/bibtex/20d01c5bfb43961426b94abbf3ffbf422/brazovayeye}, booktitle = {Late breaking paper at Genetic and Evolutionary Computation Conference {(GECCO'2006)}}, editor = {Grahl, J{\"{o}}rn}, interhash = {c1b5c8ca3fa5daedc9c4a832563659de}, intrahash = {0d01c5bfb43961426b94abbf3ffbf422}, keywords = {DE algorithms, expression gene genetic programming,}, month = {8-12 July}, notes = {Distributed on CD-ROM at GECCO-2006}, timestamp = {2008-06-19T17:55:47.000+0200}, title = {Using Differential Evolution for {GEP} Constant Creation}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp130.pdf}, year = 2006 }