An indirect block-oriented representation for genetic programming

Abstract

When Genetic Programming (GP) is applied to system identification or controller design different codings can be used for internal representation of the individuals. One common approach is a block-oriented representation where nodes of the tree structure directly correspond to blocks in a block diagram. In this paper we present an indirect block-oriented representation, which adopts some aspects of the way humans perform the modelling in order to increase the GP system's performance. A causality measure based on an edit distance is examined to compare the direct an the indirect representation. Finally, results from a real world application of the indirect block-oriented representation are presented.