Asynchronous Parallel Evolutionary Algorithms: Leveraging Heterogeneous Fitness Evaluation Times for Scalability and Elitist Parsimony Pressure
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Martin:2015:GECCOcompa,
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author = "Matthew A. Martin and Alex R. Bertels and
Daniel R. Tauritz",
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title = "Asynchronous Parallel Evolutionary Algorithms:
Leveraging Heterogeneous Fitness Evaluation Times for
Scalability and Elitist Parsimony Pressure",
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booktitle = "GECCO Companion '15: Proceedings of the Companion
Publication 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 = "978-1-4503-3488-4",
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keywords = "genetic algorithms, genetic programming: Poster",
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pages = "1429--1430",
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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/10.1145/2739482.2764718",
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DOI = "doi:10.1145/2739482.2764718",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Many important problem classes lead to large
variations in fitness evaluation times, such as is
often the case in Genetic Programming where the time
complexity of executing one individual may differ
greatly from that of another. Asynchronous Parallel
Evolutionary Algorithms (APEAs) omit the generational
synchronization step of traditional EAs which work in
well-defined cycles. This paper provides an empirical
analysis of the scalability improvements obtained by
applying APEAs to such problem classes, aside from the
speed-up caused merely by the removal of the
synchronization step. APEAs exhibit bias towards
individuals with shorter fitness evaluation times,
because they propagate faster. This paper demonstrates
how this bias can be leveraged in order to provide a
unique type of elitist parsimony pressure which rewards
more efficient solutions with equal solution quality.",
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notes = "Also known as \cite{2764718} Distributed at
GECCO-2015.",
- }
Genetic Programming entries for
Matthew A Martin
Alex R Bertels
Daniel R Tauritz
Citations