Solving symbolic regression problems with uniform design-aided gene expression programming
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- @Article{journals/tjs/ChenCKHX13,
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title = "Solving symbolic regression problems with uniform
design-aided gene expression programming",
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author = "Yunliang Chen and Dan Chen and Samee Ullah Khan and
Jianzhong Huang and Changsheng Xie",
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journal = "The Journal of Supercomputing",
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year = "2013",
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number = "3",
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volume = "66",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, GEP",
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bibdate = "2013-11-11",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/tjs/tjs66.html#ChenCKHX13",
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pages = "1553--1575",
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URL = "http://dx.doi.org/10.1007/s11227-013-0943-6",
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size = "23 pages",
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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