Evolving the autosteering of a car featuring a realistically simulated steering response

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Vsevolod Nikulin and Albert Podusenko and 
                 Ivan Tanev and Katsunori Shimohara",
  title =        "Evolving the autosteering of a car featuring a
                 realistically simulated steering response",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "2018",
  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",
  isbn13 =       "978-1-4503-5618-3",
  pages =        "1326--1332",
  address =      "Kyoto, Japan",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1145/3205455.3205547",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  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.",
  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