An improved Gene Expression Programming approach for symbolic regression problems

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@Article{journals/ijon/PengYQHS14,

• 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",
• keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Evolutionary algorithm, Symbolic regression, Data modeling",
• bibdate = "2014-05-20",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijon/ijon137.html#PengYQHS14",
• URL = "http://dx.doi.org/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 = "Key Lab of Scientific Computing and Intelligent Information Processing in Universities of Guangxi, Guangxi Teachers Education University, Nanning 530001, China",

}

Genetic Programming entries for Yu-zhong Peng Chang-an Yuan Xiao Qin Jiangtao Huang YaBing Shi

Citations