Geometric Semantic Genetic Programming with Local Search
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Castelli:2015:GECCO,
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author = "Mauro Castelli and Leonardo Trujillo and
Leonardo Vanneschi and Sara Silva and Emigdio Z-Flores and
Pierrick Legrand",
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title = "Geometric Semantic Genetic Programming with Local
Search",
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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 = "999--1006",
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keywords = "genetic algorithms, genetic programming",
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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/10.1145/2739480.2754795",
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DOI = "doi:10.1145/2739480.2754795",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Since its introduction, Geometric Semantic Genetic
Programming (GSGP) has aroused the interest of numerous
researchers and several studies have demonstrated that
GSGP is able to effectively optimize training data by
means of small variation steps, that also have the
effect of limiting overfitting. In order to speed up
the search process, in this paper we propose a system
that integrates a local search strategy into GSGP
(called GSGP-LS). Furthermore, we present a hybrid
approach, that combines GSGP and GSGP-LS, aimed at
exploiting both the optimization speed of GSGP-LS and
the ability to limit overfitting of GSGP. The
experimental results we present, performed on a set of
complex real-life applications, show that GSGP-LS
achieves the best training fitness while converging
very quickly, but severely overfits. On the other hand,
GSGP converges slowly relative to the other methods,
but is basically not affected by overfitting. The best
overall results were achieved with the hybrid approach,
allowing the search to converge quickly, while also
exhibiting a noteworthy ability to limit overfitting.
These results are encouraging, and suggest that future
GSGP algorithms should focus on finding the correct
balance between the greedy optimization of a local
search strategy and the more robust geometric semantic
operators.",
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notes = "Also known as \cite{2754795} 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
Mauro Castelli
Leonardo Trujillo
Leonardo Vanneschi
Sara Silva
Emigdio Z-Flores
Pierrick Legrand
Citations