Solving symbolic regression problems with uniform design-aided gene expression programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@Article{journals/tjs/ChenCKHX13,

• title = "Solving symbolic regression problems with uniform design-aided gene expression programming",
• author = "Yunliang Chen and Dan Chen and Samee Ullah Khan and Jianzhong Huang and Changsheng Xie",
• journal = "The Journal of Supercomputing",
• year = "2013",
• number = "3",
• volume = "66",
• keywords = "genetic algorithms, genetic programming, gene expression programming, GEP",
• bibdate = "2013-11-11",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tjs/tjs66.html#ChenCKHX13",
• pages = "1553--1575",
• URL = "http://dx.doi.org/10.1007/s11227-013-0943-6",
• size = "23 pages",
• abstract = "Gene Expression Programming (GEP) significantly surpasses traditional evolutionary approaches to solving symbolic regression problems. However, existing GEP algorithms still suffer from premature convergence and slow evolution in anaphase. Aiming at these pitfalls, we designed a novel evolutionary algorithm, namely Uniform Design-Aided Gene Expression Programming (UGEP). UGEP uses (1) a mixed-level uniform table for generating initial population and (2) multiparent crossover operators by taking advantages of the dispersibility of uniform design. In addition to a theoretic analysis, we compared UGEP to existing GEP variants via a number of experiments in dealing with symbolic regression problems including function fitting and chaotic time series prediction. Experimental results indicate that UGEP excels in terms of both the capability of achieving the global optimum and the convergence speed in solving symbolic regression problems.",

}

Genetic Programming entries for Yunliang Chen Dan Chen Samee Ullah Khan Jianzhong Huang Changsheng Xie

Citations