A new hybrid structure genetic programming in symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{shengwu:2003:anhsgpisr,

• author = "Xiong Shengwu and Wang Weiwu",
• title = "A new hybrid structure genetic programming in symbolic regression",
• booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003",
• editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon",
• pages = "1500--1506",
• year = "2003",
• publisher = "IEEE Press",
• address = "Canberra",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "8-12 " # dec,
• organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• keywords = "genetic algorithms, genetic programming, Arithmetic, Computer science, Convergence of numerical methods, Evolutionary computation, Fractals, Modelling, Regression analysis, Shape, Time varying systems, regression analysis, GP representation, complex system modelling, continuous function, discontinuity points, discontinuous function, function regression, function structure, hybrid structure genetic programming, nonsmooth function, smooth function, symbolic regression",
• DOI = "doi:10.1109/CEC.2003.1299850",
• ISBN = "0-7803-7804-0",
• abstract = "Genetic Programming (GP) has been applied to symbolic regression problem for a long time. The symbolic regression is to discover a function that can fit a finite set of sample data. These sample data can be guided by a simple function, which is continuous and smooth. But in a complex system, they can be produced by a discontinuous or non-smooth function. When conventional GP is applied to this complex system's modeling, it gets poor performance. This paper proposes a new GP representation and algorithm that can be applied to both continuous function's and discontinuous function's regression. Our approach is able to identify both simultaneously the function's structure and the discontinuity points. The numerical experimental results will show that the new GP is able to gain higher success rate, higher convergence rate and better solutions than conventional GP.",
• notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.",

}

Genetic Programming entries for Shengwu Xiong Weiwu Wang

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