S. Bain, J. Thornton, und A. Sattar. Proceedings of the 2004 IEEE Congress on Evolutionary
Computation, Seite 265--272. Portland, Oregon, IEEE Press, (20-23 June 2004)
Zusammenfassung
This paper proposes a framework for automatically
evolving constraint satisfaction algorithms using
genetic programming. The aim is to overcome the
difficulties associated with matching algorithms to
specific constraint satisfaction problems. A
representation is introduced that is suitable for
genetic programming and that can handle both complete
and local search heuristics. In addition, the
representation is shown to have considerably more
flexibility than existing alternatives, being able to
discover entirely new heuristics and to exploit
synergies between heuristics. In a preliminary
empirical study it is shown that the new framework is
capable of evolving algorithms for solving the
well-studied problem of boolean satisfiability
testing.
%0 Conference Paper
%1 bain:2004:eafcs
%A Bain, Stuart
%A Thornton, John
%A Sattar, Abdul
%B Proceedings of the 2004 IEEE Congress on Evolutionary
Computation
%C Portland, Oregon
%D 2004
%I IEEE Press
%K Combinatorial \& algorithms, genetic numerical optimization programming,
%P 265--272
%T Evolving Algorithms for Constraint Satisfaction
%U http://stuart.multics.org/publications/CEC2004.pdf
%X This paper proposes a framework for automatically
evolving constraint satisfaction algorithms using
genetic programming. The aim is to overcome the
difficulties associated with matching algorithms to
specific constraint satisfaction problems. A
representation is introduced that is suitable for
genetic programming and that can handle both complete
and local search heuristics. In addition, the
representation is shown to have considerably more
flexibility than existing alternatives, being able to
discover entirely new heuristics and to exploit
synergies between heuristics. In a preliminary
empirical study it is shown that the new framework is
capable of evolving algorithms for solving the
well-studied problem of boolean satisfiability
testing.
%@ 0-7803-8515-2
@inproceedings{bain:2004:eafcs,
abstract = {This paper proposes a framework for automatically
evolving constraint satisfaction algorithms using
genetic programming. The aim is to overcome the
difficulties associated with matching algorithms to
specific constraint satisfaction problems. A
representation is introduced that is suitable for
genetic programming and that can handle both complete
and local search heuristics. In addition, the
representation is shown to have considerably more
flexibility than existing alternatives, being able to
discover entirely new heuristics and to exploit
synergies between heuristics. In a preliminary
empirical study it is shown that the new framework is
capable of evolving algorithms for solving the
well-studied problem of boolean satisfiability
testing.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Portland, Oregon},
author = {Bain, Stuart and Thornton, John and Sattar, Abdul},
biburl = {https://www.bibsonomy.org/bibtex/25095188f6186fbdf353b9b32e9bee38b/brazovayeye},
booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary
Computation},
interhash = {c254d3fc1ad6b0adb24b3b3e05b90e04},
intrahash = {5095188f6186fbdf353b9b32e9bee38b},
isbn = {0-7803-8515-2},
keywords = {Combinatorial \& algorithms, genetic numerical optimization programming,},
month = {20-23 June},
notes = {CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.},
pages = {265--272},
publisher = {IEEE Press},
size = {8 pages},
timestamp = {2008-06-19T17:36:07.000+0200},
title = {Evolving Algorithms for Constraint Satisfaction},
url = {http://stuart.multics.org/publications/CEC2004.pdf},
year = 2004
}