Investigation of Linear Genetic Programming Techniques for Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{DBLP:conf/bracis/SottoM14,
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author = "Leo Francoso Dal Piccol Sotto and
Vinicius Veloso {de Melo}",
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title = "Investigation of Linear Genetic Programming Techniques
for Symbolic Regression",
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booktitle = "Brazilian Conference on Intelligent Systems, BRACIS
2014",
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year = "2014",
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editor = "Ricardo B. C. Prudencio and Paulo E. Santos",
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pages = "146--151",
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address = "Sao Paulo, Brazil",
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month = oct # " 18-22",
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organisation = "SBC",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4799-5618-0",
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timestamp = "Sat, 04 Oct 4429704 11:57:20 +",
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biburl = "http://dblp1.uni-trier.de/rec/bib/conf/bracis/2014",
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bibsource = "dblp computer science bibliography, http://dblp.org",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6984822",
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URL = "http://dx.doi.org/10.1109/BRACIS.2014.36",
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DOI = "doi:10.1109/BRACIS.2014.36",
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size = "6 pages",
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abstract = "In this paper, we investigate some variants of a basic
linear genetic programming (LGP) algorithm in the
problem of symbolic regression. We explore the effects
of using techniques to control bloat and to privilege a
greater percentage of effective code in the population,
individually, and examine its possibility of producing
better solutions. We also test the effects and
performance of an operator that considers two
successful individuals as sub functions and join them
into a new individual. We conduct experiments and
discuss what effects each variant introduces to the
evolution and its chance of producing better
solutions.",
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notes = "http://jcris2014.icmc.usp.br/index.php/bracis-eniac",
- }
Genetic Programming entries for
Leo Francoso Dal Piccol Sotto
Vinicius Veloso de Melo
Citations