An adaptive function identification system

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@InProceedings{Jiang:1993:afis,

• author = "Mingda Jiang and Alden H. Wright",
• title = "An adaptive function identification system",
• booktitle = "Proceedings of the IEEE/ACM Conference on Developing and Managing Intelligent System Projects, Vienna, Virginia, USA",
• year = "1993",
• pages = "47--53",
• month = mar,
• keywords = "genetic algorithms, genetic programming, Levenberg-Marquardt nonlinear regression algorithm, adaptive function identification system, adaptive system, expression-tree representation, symbolic function identification problem, adaptive systems, learning (artificial intelligence)",
• DOI = "doi:10.1109/DMISP.1993.248637",
• size = "7 pages",
• abstract = "Given data in the form of a collection of (x,y) pairs of real numbers, the symbolic function identification problem is to find a functional model of the form y=f(x) that fits the data. This paper describes an adaptive system for solution of symbolic function identification problems that combines a genetic algorithm and the Levenberg-Marquardt nonlinear regression algorithm. The genetic algorithm uses an expression-tree representation rather than the more usual binary-string representation. Experiments were run with data generated using a wide variety of function models. The system was able to find a function model that closely approximated the data with a very high success rate",
• notes = "HGSFI, Ultrix, Unidata Inc. Also known as \cite{248637}",

}

Genetic Programming entries for Mingda Jiang Alden H Wright

Citations