Abstract
The natural gene expression process evaluates the fitness of a DNA-string through a sequence of representation transformations. The genetic code defines one such transformation in this process. This paper shows that genetic code-like transformations introduce an interesting property in the representation of a genetic fitness function. It points out that such adaptive transformations can convert some functions with an exponentially large description in Fourier basis to one that is highly suitable for polynomial-size approximation. Such transformations can construct a Fourier representation with only a polynomial number of terms that axe exponentially more significant than the rest when fitter chromosomes are given more copies through a redundant, equivalent representation. This is a very desirable property [2, 3] for efficient function-induction from data which is a fundamental problem in learning, data mining, and optimization.
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Kargupta, H. (2000). The Genetic Code-Like Transformations and Their Effect on Learning Functions. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_10
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DOI: https://doi.org/10.1007/3-540-45356-3_10
Publisher Name: Springer, Berlin, Heidelberg
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