Evolving event-driven programs with SignalGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Lalejini:2018:GECCO,
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author = "Alexander Lalejini and Charles Ofria",
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title = "Evolving event-driven programs with {SignalGP}",
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booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2018",
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editor = "Hernan Aguirre and Keiki Takadama and
Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and
Andrew M. Sutton and Satoshi Ono and Francisco Chicano and
Shinichi Shirakawa and Zdenek Vasicek and
Roderich Gross and Andries Engelbrecht and Emma Hart and
Sebastian Risi and Ekart Aniko and Julian Togelius and
Sebastien Verel and Christian Blum and Will Browne and
Yusuke Nojima and Tea Tusar and Qingfu Zhang and
Nikolaus Hansen and Jose Antonio Lozano and
Dirk Thierens and Tian-Li Yu and Juergen Branke and
Yaochu Jin and Sara Silva and Hitoshi Iba and
Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and
Federica Sarro and Giuliano Antoniol and Anne Auger and
Per Kristian Lehre",
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isbn13 = "978-1-4503-5618-3",
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pages = "1135--1142",
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address = "Kyoto, Japan",
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DOI = "doi:10.1145/3205455.3205523",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming",
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abstract = "We present SignalGP, a new genetic programming (GP)
technique designed to incorporate the event-driven
programming paradigm into computational evolution's
toolbox. Event-driven programming is a software design
philosophy that simplifies the development of reactive
programs by automatically triggering program modules
(event-handlers) in response to external events, such
as signals from the environment or messages from other
programs. SignalGP incorporates these concepts by
extending existing tag-based referencing techniques
into an event-driven context. Both events and functions
are labelled with evolvable tags; when an event occurs,
the function with the closest matching tag is
triggered. In this work, we apply SignalGP in the
context of linear GP. We demonstrate the value of the
event-driven paradigm using two distinct test problems
(an environment coordination problem and a distributed
leader election problem) by comparing SignalGP to
variants that are otherwise identical, but must
actively use sensors to process events or messages. In
each of these problems, rapid interaction with the
environment or other agents is critical for maximizing
fitness. We also discuss ways in which SignalGP can be
generalized beyond our linear GP implementation.",
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notes = "Also known as \cite{3205523} GECCO-2018 A
Recombination of the 27th International Conference on
Genetic Algorithms (ICGA-2018) and the 23rd Annual
Genetic Programming Conference (GP-2018)",
- }
Genetic Programming entries for
Alexander Lalejini
Charles Ofria
Citations