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Evolving a Statistics Class Using Object Oriented Evolutionary Programming

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Genetic Programming (EuroGP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4445))

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Abstract

Object Oriented Evolutionary Programming is used to evolve programs that calculate some statistical measures on a set of numbers. We compared this technique with a more standard functional representation. We also studied the effects of scalar and Pareto-based multi-objective fitness functions to the induction of multi-task programs. We found that the induction of a program residing in an OO representation space is more efficient, yielding less fitness evaluations, and that scalar fitness performed better than Pareto-based fitness in this problem domain.

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References

  1. Booch, G.: Object-Oriented Analysis and Design with applications, 2nd edn. Object Technology series. Addison Wesley, Reading

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Authors and Affiliations

  1. Department of Computer Science, University of Essex, Colchester CO4 3SQ, UK

    Alexandros Agapitos & Simon M. Lucas

Authors
  1. Alexandros Agapitos
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  2. Simon M. Lucas
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Editor information

Marc Ebner Michael O’Neill Anikó Ekárt Leonardo Vanneschi Anna Isabel Esparcia-Alcázar

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© 2007 Springer Berlin Heidelberg

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Agapitos, A., Lucas, S.M. (2007). Evolving a Statistics Class Using Object Oriented Evolutionary Programming. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_27

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  • DOI: https://doi.org/10.1007/978-3-540-71605-1_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71602-0

  • Online ISBN: 978-3-540-71605-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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