Indirect Online Evolution - A Conceptual Framework for Adaptation in Industrial Robotic Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{furuholmen2008indirect,
  author =       "Marcus Furuholmen and Kyrre Glette and 
                 Jim Torresen and Mats Hovin",
  title =        "Indirect Online Evolution - A Conceptual Framework for
                 Adaptation in Industrial Robotic Systems",
  booktitle =    "8th International Conference on Evolvable Systems:
                 From Biology to Hardware, ICES 2008",
  year =         "2008",
  editor =       "Gregory Hornby and Lukas Sekanina and 
                 Pauline C. Haddow",
  series =       "Lecture Notes in Computer Science",
  volume =       "5216",
  pages =        "165--176",
  address =      "Prague, Czech Republic",
  month =        sep # " 21-24",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-85856-0",
  DOI =          "doi:10.1007/978-3-540-85857-7_15",
  size =         "12 pages",
  abstract =     "A conceptual framework for online evolution in robotic
                 systems called Indirect Online Evolution (IDOE) is
                 presented. A model specie automatically infers models
                 of a physical system and a parameter specie
                 simultaneously optimises the parameters of the inferred
                 models according to a specified target behaviour.
                 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, hence limiting both the evaluation time, the
                 wear out and the potential hazards normally associated
                 with direct online evolution (DOE), where every
                 candidate solution has to be evaluated on the physical
                 system. Features of IDOE are demonstrated by inferring
                 models of a simple hidden system containing geometric
                 shapes that are further optimized according to a target
                 value. Simulated experiments indicate that the fitness
                 of the IDOE approach is generally higher than the
                 average fitness of DOE.",
  notes =        "ICES",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
}

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

Citations