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Genetic Programming with Adaptive Representations
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@TechReport{Rosca:TR94,
  author = 	 {Justinian P. Rosca and Dana H. Ballard},
  title = 	 {Genetic Programming with Adaptive Representations},
  institution =  {Computer Science Dept University of Rochester},
  year = 	 {1994},
  number = 	 {489},
  address = 	 {Rochester NY},
  month = 	 {February},
  abstract =     {
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.   }

}