A Multi-objective Approach for Symbolic Regression with Semantic Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Casadei:2019:BRACIS,
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author = "Felipe Casadei and Joao Francisco B. S. Martins and
Gisele L. Pappa",
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title = "A Multi-objective Approach for Symbolic Regression
with Semantic Genetic Programming",
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booktitle = "2019 8th Brazilian Conference on Intelligent Systems
(BRACIS)",
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year = "2019",
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pages = "66--71",
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month = "15-18 " # oct,
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address = "Salvador, Brazil",
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keywords = "genetic algorithms, genetic programming, Geometric
Semantic Genetic Programming, Symbolic Regression,
Training, Semantic search, Sociology, Statistics,
Predictive models, Multi-ojective Optimization",
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isbn13 = "978-1-7281-4254-8",
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ISSN = "2643-6264",
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DOI = "doi:10.1109/BRACIS.2019.00021",
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size = "6 pages",
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abstract = "This paper proposes a multi-objective approach for
solving symbolic regression problems using Geometric
Semantic Genetic Programming (GSGP). The proposed
method produces models specialized in smaller regions
of the semantic search space, where the errors of the
models into these different regions are the objectives
being optimized. The method incorporates different ways
of defining these sub-regions of the semantic space as
well as a method to combine the models found intending
to produce a unique prediction. Experimental results
obtained over 10 real-world datasets show that the
proposed method outperforms traditional GSGP in 7 out
of 10 datasets.",
-
notes = "Also known as \cite{8923924}",
- }
Genetic Programming entries for
Felipe Casadei
Joao Francisco B S Martins
Gisele L Pappa
Citations