- author = "YuZhong Peng and ChangAn Yuan and Xiao Qin and JiangTao Huang and YaBing Shi",
- title = "An improved Gene Expression Programming approach for symbolic regression problems",
- journal = "Neurocomputing",
- year = "2014",
- volume = "137",
- pages = "293--301",
- month = "5 " # aug,
- note = "Advanced Intelligent Computing Theories and Methodologies",
- keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Evolutionary algorithm, Symbolic regression, Data modeling",
- ISSN = "0925-2312",
- bibdate = "2014-05-20",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijon/ijon137.html#PengYQHS14",
-
URL = "
https://www.sciencedirect.com/science/article/pii/S0925231214002598",
-
DOI = "
doi:10.1016/j.neucom.2013.05.062",
- abstract = "Gene Expression Programming (GEP) is a powerful evolutionary method for knowledge discovery and model learning. Based on the basic GEP algorithm, this paper proposes an improved algorithm named S_GEP, which is especially suitable for dealing with symbolic regression problems. The major advantages for this S_GEP method include: (1) A new method for evaluating individual without expression tree; (2) a corresponding expression tree construction schema for the new evaluating individual method if required by some special complex problems; and (3) a new approach for manipulating numeric constants so as to improve the convergence. A thorough comparative study between our proposed S_GEP method with the primitive GEP, as well as other methods are included in this paper. The comparative results show that the proposed S_GEP method can significantly improve the GEP performance. Several well-studied benchmark test cases and real-world test cases demonstrate the efficiency and capability of our proposed S_GEP for symbolic regression problems.",
-
notes = "Also known as \cite{PENG2014293}
Part of special issue:Advanced Intelligent Computing Theories and Methodologies: Selected papers from the 2012 Eighth International Conference on Intelligent Computing (ICIC 2012)
Key Lab of Scientific Computing and Intelligent Information Processing in Universities of Guangxi, Guangxi Teachers Education University, Nanning 530001, China",
