System Identification Approach to Genetic Programming

Abstract

This paper introduces a new approach to Genetic Programming (GP), based on a system identification technique, which integrates a GP-based adaptive search of tree structures, and a local parameter tuning mechanism employing statistical search. In traditional GP, recombination can cause frequent disruption of building-blocks or mutation can cause abrupt changes in the semantics. To overcome these difficulties, we supplement traditional GP with a recovery mechanism of disrupted building-blocks. More precisely, we integrate the structural search of traditional GP with a local hill-climbing search, using a relabeling procedure. In an earlier paper, we introduced our adaptive program called "STROGANOFF" (i.e. STructured Representation On Genetic Algorithms for NOnlinear Function Fitting), which integrated a multiple regression analysis method and a GA-based search strategy. The effectiveness of STROGANOFF was demonstrated by solving several system identification (numerical) problems. This paper extends STROGANOFF to symbolic (non-numerical) reasoning, by introducing multiple types of nodes, using a modified "Minimum Description Length (MDL)" -based selection criterion, and a pruning of the resultant trees. The eflectiveness of this system-identification approach to GP is demonstrated by successful application to time-series prediction and to symbolic regression problems.