Evaluating GP schema in context

ABSTRACT

We propose a new methodology to look at the fitness contributions (semantics) of different schemata in Genetic Programming (GP). We hypothesize that the significance of a schema can be evaluated by calculating its fitness contribution to the total fitness of the trees that contain it, and use our methodology to test this hypothesis.It is shown that this method can also be used to identify schemata that are important in terms of both individual runs and individual problems (that is, schema that will be important across many runs on a particular problem).The usefulness of this study to existing schema theories and its effective use in the detection of introns, in the identification of potentially useful modular functions are also discussed in this paper.

REFERENCES

• 1: Peter J. Angeline, Genetic programming and emergent intelligence, Advances in genetic programming, MIT Press, Cambridge, MA, 1994
• 2: Hitoshi Iba , Hugo de Garis, Extending genetic programming with recombinative guidance, Advances in genetic programming: volume 2, MIT Press, Cambridge, MA, 1996
• 3: Justinian P. Rosca , Dana H. Ballard, Discovery of subroutines in genetic programming, Advances in genetic programming: volume 2, MIT Press, Cambridge, MA, 1996

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

General Terms:
Algorithms, Theory

Keywords:
genetic programming, module acquisition, schema theory, tree semantics