Some Experimental Results with Tree Adjunct Grammar Guided Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
An Adaptive GP Strategy for Evolving Digital Circuits
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
An autonomous GP-based system for regression and classification problems
Applied Soft Computing
Functional modularity for genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
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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.