Genetic Programming with Adaptive Representations

Machine learning aims towards the acquisition of knowledge based on either experience from the interaction with the external environment or by analyzing the internal problem-solving traces. Both approaches can be implemented in the Genetic Programming (GP) paradigm. Hillis [1990] proves in an ingenious way how the first approach can work. There have not been any significant tests to prove that GP can take advantage of its own search traces. This paper presents an approach to automatic discovery of functions in GP based on the ideas of discovery of useful building blocks by analyzing the evolution trace, generalizing of blocks to define new functions and finally adapting of the problem representation on-the-fly. Adaptation of the representation determines a hierarchical organization of the extended function set which enables a restructuring of the search space so that solutions can be found more easily. Complexity measures of solution trees are defined for an adaptive representation framework and empirical results are presented.