Evaluation de systemes robotiques et comportements complexes par algorithmes evolutionnaires

Created by W.Langdon from gp-bibliography.bib Revision:1.4215

  author =       "Frederic Chapelle",
  title =        "Evaluation de systemes robotiques et comportements
                 complexes par algorithmes evolutionnaires",
  school =       "University Pierre et Marie Curie, Paris VI",
  month =        sep,
  year =         "2002",
  address =      "France",
  note =         "in french",
  keywords =     "genetic algorithms, genetic programming,
                 Computer-aided design, robotic systems, simultaneous
                 structure/control evaluation, symbolic regression,
                 inverse models, inverse kinematic problem, programming,
                 control, simulation, medical devices, minimally
                 invasive surgery",
  URL =          "http://www.sudoc.fr/069898715",
  abstract =     "Evaluation of robotic systems and complex behaviours
                 using evolutionary algorithms : in this thesis, an
                 original approach for evaluation of robotic systems in
                 the context of simultaneous structure/control design is
                 presented. It relies on the evolutionary algorithms.
                 The initial procedures for evaluation are usually
                 difficult to implement and expensive in computing time.
                 The developed method uses genetic programming within an
                 evolutionary symbolic regression algorithm, to generate
                 expressions with various levels of refinement which are
                 intended to approximate the original evaluations
                 (according to the concept of metamodels). The interest
                 of this approach is illustrated by various applications
                 of gradual complexity where the initial evaluation
                 methods can be simple functions, algorithms or a value
                 drawn from a simulation considering the globality of
                 the system to be designed, its interactions with the
                 environment and its tasks. Reliable and fast generic
                 models, which are solutions of the inverse kinematic
                 problem for any 6R manipulator geometry (analytical or
                 not), have been produced via approximating functions.
                 The application of these techniques to a problem with
                 dynamics resulted in fixing restrictions to the use of
                 our method for direct approximation of constrained
                 behaviours. Evolutionary symbolic regression is then
                 applied within the framework of optimisations by
                 genetic algorithms (GA), for simple cases like when a
                 GA seeks a solution of the 2D inverse kinematic
                 problem, or more complex like preliminary design of
                 smart active endoscopes for minimally invasive surgery.
                 Additionally, an extension allowing to increase the
                 evolutionarity of GA is deduced.",
  notes =        "Supervisor Philippe Bidaud",

Genetic Programming entries for Frederic Chapelle