A Hierarchical Genetic System for Symbolic Function Identification
نویسنده
چکیده
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 a 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.
منابع مشابه
Statistical Investigations of Genetic Algorithms and Genetic Programming
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 a system for solution of symbolic function identification problems that combines a genetic algorithm and the Levenberg-Marquardt nonlinear regression algorithm. The genetic algorithm uses a...
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