Evaluation de systemes robotiques et comportements complexes par algorithmes evolutionnaires

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@PhdThesis{Chapelle:2002:thesis,

• 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

Citations