Improving Symbolic Regression through a semantics-driven framework
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- @InProceedings{Huynh:2016:SSCI,
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author = "Quang Nhat Huynh and Hemant Kumar Singh and
Tapabrata Ray",
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booktitle = "2016 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "Improving Symbolic Regression through a
semantics-driven framework",
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year = "2016",
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abstract = "The process of identifying analytical relationships
among variables and responses in observed data is
commonly referred to as Symbolic Regression (SR).
Genetic Programming is one of the commonly used
approaches for SR, which operates by evolving
expressions. Such relationships could be explicit or
implicit in nature, of which the former has been more
extensively studied in literature. Even though
extensive studies have been done in SR, the fundamental
challenges such as bloat, loss of diversity and
accurate determination of coefficients still persist.
Recently, semantics and multi-objective formulation
have been suggested as potential tools to alleviate
these issues by building more intelligence in the
search process. However, studies along both these
directions have been in isolation and applied only to
selected components of SR so far. In this paper, we
intend to build a framework that integrates semantics
deeper into more components of SR. The framework could
be operated in conventional single objective as well as
multi-objective mode and is capable of dealing with
both explicit and implicit functions. Semantics are
used in the proposed framework for improving
compactness and diversity of expressions, crossover and
local exploitation. Numerical experiments are presented
on a set of benchmark problems to demonstrate the
strengths of the proposed approach.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI.2016.7849941",
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month = dec,
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notes = "Also known as \cite{7849941}",
- }
Genetic Programming entries for
Quang Nhat Huynh
Hemant Kumar Singh
Tapabrata Ray
Citations