Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Hein:2018:GECCOcomp,
  author =       "Daniel Hein and Steffen Udluft and Thomas A. Runkler",
  title =        "Generating interpretable fuzzy controllers using
                 particle swarm optimization and genetic programming",
  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 =        "1268--1275",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205651.3208277",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Autonomously training interpretable control
                 strategies, called policies, using pre-existing plant
                 trajectory data is of great interest in industrial
                 applications. Fuzzy controllers have been used in
                 industry for decades as interpretable and efficient
                 system controllers. In this study, we introduce a fuzzy
                 genetic programming (GP) approach called fuzzy GP
                 reinforcement learning (FGPRL) that can select the
                 relevant state features, determine the size of the
                 required fuzzy rule set, and automatically adjust all
                 the controller parameters simultaneously. Each GP
                 individual's fitness is computed using model-based
                 batch reinforcement learning (RL), which first trains a
                 model using available system samples and subsequently
                 performs Monte Carlo roll outs to predict each policy
                 candidate's performance. We compare FGPRL to an
                 extended version of a related method called fuzzy
                 particle swarm reinforcement learning (FPSRL), which
                 uses swarm intelligence to tune the fuzzy policy
                 parameters. Experiments using an industrial benchmark
                 show that FGPRL is able to autonomously learn
                 interpretable fuzzy policies with high control
                 performance.",
  notes =        "Also known as \cite{3208277} 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 Daniel Hein Steffen Udluft Thomas A Runkler

Citations