Learning recursive functions with object oriented genetic programming

  • Authors:
  • Alexandros Agapitos;Simon M. Lucas

  • Affiliations:
  • Department of Computer Science, University of Essex, Colchester, UK;Department of Computer Science, University of Essex, Colchester, UK

  • Venue:
  • EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
  • Year:
  • 2006

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Abstract

This paper describes the evolution of recursive functions within an Object-Oriented Genetic Programming (OOGP) system. We evolved general solutions to factorial, Fibonacci, exponentiation, even-n-Parity, and nth-3. We report the computational effort required to evolve these methods and provide a comparison between crossover and mutation variation operators, and also undirected random search. We found that the evolutionary algorithms performed much better than undirected random search, and thats mutation outperformed crossover on most problems.