Abstract
This chapter introduces a new approach to Genetic Programming (GP), based on GMDH-based technique, which integrates a GP-based adaptive search of tree structures, and a local parameter tuning mechanism employing statistical search. The GP is supplemented with a local hill climbing search, using a parameter tuning procedure. More precisely, we integrate the structural search of traditional GP with a multiple regression analysis method and establish our adaptive program called .STROGANOFF’ (i.e. STructured Representation On Genetic Algorithms for NOnlinear Function Fitting). The fitness evaluation is based on aMinimumDescription Length (MDL) criterion, which effectively controls the tree growth in GP. Its effectiveness is demonstrated by solving several system identification (numerical) problems and comparinf the performance of STROGANOFF with traditional GP and another standard technique. The effectiveness of this numerical approach to GP is demonstrated by successful application to computational finances.
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