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ABSTRACT

Elitism is commonly used in generational GP to ensure that the best individuals discovered in a generation are not lost, and are made available for possible further improvements to new generations. Using two GP systems and four problems, we show how elitism reduces the growth of mean program size.

REFERENCES

• 1: John R. Koza, Genetic programming: on the programming of computers by means of natural selection, MIT Press, Cambridge, MA, 1992
• 2: John R. Koza, Genetic programming II: automatic discovery of reusable programs, MIT Press, Cambridge, MA, 1994
• 3: Foundations of genetic programming, Springer-Verlag New York, Inc., New York, NY, 2002
• 4: Alan Piszcz , Terence Soule, Dynamics of evolutionary robustness, Proceedings of the 8th annual conference on Genetic and evolutionary computation, July 08-12, 2006, Seattle, Washington, USA [doi>10.1145/1143997.1144149]
• 5: R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008. (With contributions by J. R. Koza).

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.2 Automatic Programming

General Terms:
Algorithms, Performance

Keywords:
bloat, elitism, genetic programming