Continuous Adaptation in Robotic Systems by Indirect Online Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{furuholmen2008continuous,

• 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

Citations