A Semantics based Symbolic Regression Framework for Mining Explicit and Implicit Equations from Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Huynh:2016:GECCOcomp,
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author = "Quang Nhat Huynh and Hemant Kumar Singh and
Tapabrata Ray",
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title = "A Semantics based Symbolic Regression Framework for
Mining Explicit and Implicit Equations from Data",
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booktitle = "GECCO '16 Companion: Proceedings of the Companion
Publication of the 2016 Annual Conference on Genetic
and Evolutionary Computation",
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year = "2016",
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editor = "Tobias Friedrich and Frank Neumann and
Andrew M. Sutton and Martin Middendorf and Xiaodong Li and
Emma Hart and Mengjie Zhang and Youhei Akimoto and
Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and
Daniele Loiacono and Julian Togelius and
Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and
Faustino Gomez and Carlos M. Fonseca and
Heike Trautmann and Alberto Moraglio and William F. Punch and
Krzysztof Krawiec and Zdenek Vasicek and
Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and
Boris Naujoks and Enrique Alba and Gabriela Ochoa and
Simon Poulding and Dirk Sudholt and Timo Koetzing",
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pages = "103--104",
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month = "20-24 " # jul,
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keywords = "genetic algorithms, genetic programming: Poster",
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organisation = "SIGEVO",
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address = "Denver, USA",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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isbn13 = "978-1-4503-4323-7",
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DOI = "doi:10.1145/2908961.2908989",
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abstract = "Symbolic Regression (SR) is commonly used to identify
relationships among variables and responses in a data
in the form of analytical, preferably compact
expressions. Genetic Programming (GP) is one of the
common ways to perform SR. Such relationships could be
represented using explicit or implicit expressions, of
which the former has been more extensively studied in
literature. Some of the key challenges that face SR are
bloat, loss of diversity, and accurate determination of
coefficients. More recently, semantics and
multi-objective formulations have been suggested as
potential tools to build 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 traditional single objective as well as
multi-objective mode and is capable of dealing with
both explicit and implicit functions. The constituent
modules use semantics for compaction of expressions,
maintaining diversity by identifying unique
individuals, crossover and local exploitation. A
comparison of obtained results with those from existing
semantics-based and multi-objective approach
demonstrates the advantages of the proposed
framework.",
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notes = "Distributed at GECCO-2016.",
- }
Genetic Programming entries for
Quang Nhat Huynh
Hemant Kumar Singh
Tapabrata Ray
Citations