Genetic Programming with Adaptive Representations
TR 489. University of Rochester, Computer Science Department, Rochester, NY, USA, (Februar 1994)Zusammenfassung
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. Hillis90 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.