Evolving the autosteering of a car featuring a realistically simulated steering response
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gp-bibliography.bib Revision:1.8028
- @InProceedings{Nikulin:2018:GECCO,
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author = "Vsevolod Nikulin and Albert Podusenko and
Ivan Tanev and Katsunori Shimohara",
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title = "Evolving the autosteering of a car featuring a
realistically simulated steering response",
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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 = "1326--1332",
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address = "Kyoto, Japan",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1145/3205455.3205547",
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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 consider the area of intelligent road vehicles,
especially, the topic of automated vehicles. Focusing
on the importance of the automated steering, we address
the challenge of automated keeping of a car in the
middle of the driving lane. Our objective is to
investigate the feasibility of employing genetic
programming (GP) to evolve the automated steering of a
car. The latter is implemented in the Open Source
Racing Car Simulator (TORCS) with a realistically
modelled steering featuring both a delay of response
and a rate limit. We propose two approaches aimed at
improving the efficiency of evolution via GP. In the
first approach we implement an incremental evolution of
the steering function by commencing the evolution with
an ideal car and gradually increasing the degree of its
realism (i.e., the amount of steering delay) in due
course of evolution. The second approach is based on
incorporating expert knowledge about the (expected)
structure of the steering function according to the
servo control model of steering. The experimental
results verify that the proposed approaches yield an
improved efficiency of evolution in that the obtained
solutions are both of a better quality and could be
obtained faster than those of the canonical GP.",
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notes = "Also known as \cite{3205547} 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
Vsevolod Nikulin
Albert Podusenko
Ivan T Tanev
Katsunori Shimohara
Citations