Fractional Genetic Programming with Probability Density Data
نویسنده
چکیده
We extend the fractional genetic programming scheme with data elements that are no more scalar, but instead are similar to probability density functions. The extension straightforwardly fits into fractional programming, in which data elements are blended from several values. In the case of our previous work, the blend produced a single scalar value. The extension proposes to build an approximate probability density function out of the blended elements. The extension turned out to be very effective in an unsuspected way: when a data element, despite being destined to approximate a probability density, represented a single–dimensional image of spatial data.
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