Analysis and Extension of the Inc* on the Satisfiability Testing Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Bader-El-Den:2008:WCCI,
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author = "Mohamed Bader-El-Den and Riccardo Poli",
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title = "Analysis and Extension of the Inc* on the
Satisfiability Testing Problem",
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booktitle = "2008 IEEE World Congress on Computational
Intelligence",
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year = "2008",
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editor = "Jun Wang",
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pages = "3342--3349",
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address = "Hong Kong",
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month = "1-6 " # jun,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, SAT",
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isbn13 = "978-1-4244-1823-7",
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file = "EC0725.pdf",
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DOI = "doi:10.1109/CEC.2008.4631250",
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abstract = "Inc (star) is a general algorithm that can be used in
conjunction with any local search heuristic and that
has the potential to substantially improve the overall
performance of the heuristic. The general idea of the
algorithm is the following. Rather than attempting to
directly solve a difficult problem, the algorithm
dynamically chooses a smaller instance of the problem,
and then increases the size of the instance only after
the previous simplified instances have been solved,
until the full size of the problem is reached. Genetic
programming is used to discover new strategies for
Inc*. Preliminary experiments on the
satisfiability problem (SAT) problem have shown that
Inc* is a competitive approach. In this
paper we enhance Inc* and we experimentally
test it on larger set of benchmarks, including big
instances of SAT. Furthermore, we provide an analysis
of the algorithm's behaviour.",
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notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
- }
Genetic Programming entries for
Mohamed Bahy Bader-El-Den
Riccardo Poli
Citations