Continuous Adaptation in Robotic Systems by Indirect Online Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Marcus Furuholmen and Mats Hovin and Jim Torresen and 
                 Kyrre Glette",
  title =        "Continuous Adaptation in Robotic Systems by Indirect
                 Online Evolution",
  booktitle =    "ECSIS Symposium on Learning and Adaptive Behaviors for
                 Robotic Systems, LAB-RS 2008",
  year =         "2008",
  pages =        "71--76",
  address =      "Edinburgh",
  month =        "6-8 " # aug,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Automatic testing, Erbium, Gene
                 expression, Informatics, Robot sensing systems,
                 Robotics and automation, Sensor phenomena and
                 characterisation, Sensor systems, System testing, US
                 Department of Energy, adaptive systems, end effectors,
                 vectors, continuous system identification, end
                 effector, indirect online evolution, parameter
                 optimisation, robotic arm, training vectors, Indirect
                 Online Evolution, Machine Learning, Robotics",
  isbn13 =       "978-0-7695-3272-1",
  DOI =          "doi:10.1109/LAB-RS.2008.13",
  size =         "6 pages",
  abstract =     "A conceptual framework for on line evolution in
                 robotic systems called indirect online evolution (IDOE)
                 is presented. A model specie automatically infers
                 models of a hidden physical system by the use of gene
                 expression programming (GEP). A parameter specie
                 simultaneously optimises the parameters of the inferred
                 models according to a specified target vector. Training
                 vectors required for modelling are automatically
                 provided online by the interplay between the two
                 coevolving species and the physical system. At every
                 generation, only the estimated fittest individual of
                 the parameter specie is executed on the physical
                 system. This approach thus limits both the evaluation
                 time, the wear out and the potential hazards normally
                 associated with direct online evolution (DOE) where
                 every individual has to be evaluated on the physical
                 system. Additionally, the approach enables continuous
                 system identification and adaptation during normal
                 operation. Features of IDOE are illustrated by
                 inferring models of a simplified, robotic arm, and
                 further optimising the parameters of the system
                 according to a target position of the end effector.
                 Simulated experiments indicate that the fitness of the
                 IDOE approach is generally higher than the average
                 fitness of DOE.",
  notes =        "Also known as \cite{4599430}",

Genetic Programming entries for Marcus Furuholmen Mats Erling Hovin Jim Torresen Kyrre Harald Glette