Iterated Function Systems Optimization with Genetic Algorithms
نویسنده
چکیده
This paper presents a genetic algorithm used to infer Iterated Function Systems (IFSs) for approximation of 1D and 2D data sets. First, we give an introduction to IFSs. Secondly, we discuss the encoding of 1D and 2D IFSs in the form of chromosomes, and present genetic operators to work upon these representations. The performance of the genetic algorithm is evaluated on four 1D and two 2D test sets. Particularly in the 2D case, the algorithm turns out to perform quite poorly. We give a theoretical explanation for these results, and provide empirical support for this explanation.
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تاریخ انتشار 2007