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Abstract:
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Symbolic regression is the search for mathematical functions that fit some data, using primitive functions such as addition, multiplication, etc. Commonly, genetic programming is used to perform such a symbolic regression. The first part of the tutorial will focus on issues of representation, operators, evaluation, size and numerical stability. The second part will focus on error measures and will exemplify the commonality between symbolic regression, error measures, penalty methods and Bayesian modelling.
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