A Local Search Approach to Genetic Programming for Binary Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Z-Flores:2015:GECCO,
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author = "Emigdio Z-Flores and Leonardo Trujillo and
Oliver Schuetze and Pierrick Legrand",
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title = "A Local Search Approach to Genetic Programming for
Binary Classification",
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booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2015",
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editor = "Sara Silva and Anna I Esparcia-Alcazar and
Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and
Christine Zarges and Luis Correia and Terence Soule and
Mario Giacobini and Ryan Urbanowicz and
Youhei Akimoto and Tobias Glasmachers and
Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and
Marta Soto and Carlos Cotta and Francisco B. Pereira and
Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and
Heike Trautmann and Jean-Baptiste Mouret and
Sebastian Risi and Ernesto Costa and Oliver Schuetze and
Krzysztof Krawiec and Alberto Moraglio and
Julian F. Miller and Pawel Widera and Stefano Cagnoni and
JJ Merelo and Emma Hart and Leonardo Trujillo and
Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and
Carola Doerr",
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isbn13 = "978-1-4503-3472-3",
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pages = "1151--1158",
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keywords = "genetic algorithms, genetic programming, Integrative
Genetic and Evolutionary Computation",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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URL = "http://doi.acm.org/10.1145/2739480.2754797",
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DOI = "doi:10.1145/2739480.2754797",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "In standard genetic programming (GP), a search is
performed over a syntax space defined by the set of
primitives, looking for the best expressions that
minimize a cost function based on a training set.
However, most GP systems lack a numerical optimization
method to fine tune the implicit parameters of each
candidate solution. Instead, GP relies on more
exploratory search operators at the syntax level. This
work proposes a memetic GP, tailored for binary
classification problems. In the proposed method, each
node in a GP tree is weighted by a real-valued
parameter, which is then numerically optimized using a
continuous transfer function and the Trust Region
algorithm is used as a local search method.
Experimental results show that potential classifiers
produced by GP are improved by the local searcher, and
hence the overall search is improved achieving
significant performance gains, that are competitive
with state-of-the-art methods on well-known
benchmarks.",
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notes = "Also known as \cite{2754797} GECCO-2015 A joint
meeting of the twenty fourth international conference
on genetic algorithms (ICGA-2015) and the twentith
annual genetic programming conference (GP-2015)",
- }
Genetic Programming entries for
Emigdio Z-Flores
Leonardo Trujillo
Oliver Schuetze
Pierrick Legrand
Citations