EVOLUTIONARY PROGRAMMING GUIDED BY ANALYTICALLY GENERATED SEEDS

Neil Crossley, Emanuel Kitzelmann, Martin Hofmann, Ute Schmid

Abstract

Evolutionary programming is the most powerful method for inducing recursive functional programs from input/output examples while taking into account efficiency and complexity constraints for the target program. However, synthesis time can be considerably high. A strategy which is complementary to the generate-and -test based approaches of evolutionary programming is inductive analytical programming where program construction is example-driven, that is, target programs are constructed as minimal generalization over the given input/output examples. Synthesis with analytical approaches is fast, but the scope of synthesizable programs is restricted. We propose to combine both approaches in such a way that the power of evolutionary programming is preserved and synthesis becomes more efficient. We use the analytical system IGOR2 to generate seeds in form of program skeletons to guide the evolutionary system ADATE when searching for target programs. In an evaluations with several examples we can show that using such seeds indeed can speed up evolutionary programming considerably.

References

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Paper Citation


in Harvard Style

Crossley N., Kitzelmann E., Hofmann M. and Schmid U. (2009). EVOLUTIONARY PROGRAMMING GUIDED BY ANALYTICALLY GENERATED SEEDS . In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009) ISBN 978-989-674-014-6, pages 197-202. DOI: 10.5220/0002286301970202


in Bibtex Style

@conference{icec09,
author={Neil Crossley and Emanuel Kitzelmann and Martin Hofmann and Ute Schmid},
title={EVOLUTIONARY PROGRAMMING GUIDED BY ANALYTICALLY GENERATED SEEDS},
booktitle={Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009)},
year={2009},
pages={197-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002286301970202},
isbn={978-989-674-014-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009)
TI - EVOLUTIONARY PROGRAMMING GUIDED BY ANALYTICALLY GENERATED SEEDS
SN - 978-989-674-014-6
AU - Crossley N.
AU - Kitzelmann E.
AU - Hofmann M.
AU - Schmid U.
PY - 2009
SP - 197
EP - 202
DO - 10.5220/0002286301970202