On Vehicle Surrogate Learning with Genetic Programming Ensembles

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

@InProceedings{Parque:2018:GECCOcomp2,
  author =       "Victor Parque and Tomoyuki Miyashita",
  title =        "On Vehicle Surrogate Learning with Genetic Programming
                 Ensembles",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  year =         "2018",
  editor =       "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and 
                 Shigeru Obayashi and Bogdan Filipic and 
                 Thomas Bartz-Beielstein and Grant Dick and 
                 Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and 
                 Pablo Valledor Pellicer and Manuel Lopez-Ibanez and 
                 Daniel R. Tauritz and Pietro S. Oliveto and 
                 Thomas Weise and Borys Wrobel and Ales Zamuda and 
                 Anne Auger and Julien Bect and Dimo Brockhoff and 
                 Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and 
                 Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and 
                 Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and 
                 Richard Duro and Joshua Auerbach and 
                 Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and 
                 Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and 
                 Francisco {Chavez de la O} and Ozgur Akman and 
                 Khulood Alyahya and Juergen Branke and Kevin Doherty and 
                 Jonathan Fieldsend and Giuseppe Carlo Marano and 
                 Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and 
                 Stefan Wagner and Michael Affenzeller and 
                 Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and 
                 Riyad Alshammari and Tokunbo Makanju and 
                 Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and 
                 John R. Woodward and Shin Yoo and John McCall and 
                 Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and 
                 Masaya Nakata and Anthony Stein and 
                 Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and 
                 Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and William {La Cava} and 
                 Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and 
                 Ivanoe {De Falco} and Antonio {Della Cioppa} and 
                 Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and 
                 Giovanni Iacca and Ahmed Hallawa and Anil Yaman and 
                 Alma Rahat and Handing Wang and Yaochu Jin and 
                 David Walker and Richard Everson and Akira Oyama and 
                 Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and 
                 Pramudita Satria Palar",
  isbn13 =       "978-1-4503-5764-7",
  pages =        "1704--1710",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205651.3208310",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming, Surrogate
                 Function, Vehicle Clusters",
  isbn13 =       "978-1-4503-5764-7",
  URL =          "http://www.human-competitive.org/sites/default/files/parque-vehicles-paper.pdf",
  URL =          "http://gecco-2018.sigevo.org/index.html/tiki-index.php?page=Workshop+Papers",
  DOI =          "doi:10.1145/3205651.3208310",
  size =         "7 pages",
  abstract =     "Learning surrogates for product design and
                 optimization is potential to capitalize on competitive
                 market segments. In this paper we propose an approach
                 to learn surrogates of product performance from
                 historical clusters by using ensembles of Genetic
                 Programming. By using computational experiments
                 involving more than 500 surrogate learning instances
                 and 27858 observations of vehicle models collected over
                 the last thirty years shows (1) the feasibility to
                 learn function surrogates as symbolic ensembles at
                 different levels of granularity of the hierarchical
                 vehicle clustering, (2) the direct relationship of the
                 predictive ability of the learned surrogates in both
                 seen (training) and unseen (testing) scenarios as a
                 function of the number of cluster instances, and (3)
                 the attractive predictive ability of relatively smaller
                 ensemble of trees in unseen scenarios. We believe our
                 approach introduces the building blocks to further
                 advance on studies regarding data-driven product design
                 and market segmentation.",
  notes =        "WS RWACMO
                 http://www.ifs.tohoku.ac.jp/edge/gecco2018-ws/program/

                 Entered for 2018 HUMIES

                 Distributed at GECCO-2018.

                 Also known as \cite{Parque:2018:GECCOcomp}
                 \cite{3208310} 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 Victor Parque Tomoyuki Miyashita

Citations