- author = "Marco Cavaglia and Kai Staats and Luciano Errico and Kentaro Mogushi and Hunter Gabbard",
- title = "{LIGO} detector characterization with genetic programming",
- booktitle = "APS April Meeting 2017",
- year = "2017",
- pages = "Abstract: X6.00008",
- address = "Washington, DC, USA",
- month = jan # " 28-31",
- note = "Bulletin of the American Physical Society",
- keywords = "genetic algorithms, genetic programming, Laser Interferometer Gravitational-Wave",
-
URL = "
http://absimage.aps.org/image/APR17/MWS_APR17-2016-000316.pdf",
-
URL = "
http://meetings.aps.org/link/BAPS.2017.APR.X6.8",
-
URL = "http://meetings.aps.org/Meeting/APR17/Session/X6.8",
- size = "0.25 pages",
- abstract = "Genetic Programming (GP) is a supervised approach to Machine Learning. GP has for two decades been applied to a diversity of problems, from predictive and financial modelling to data mining, from code repair to optical character recognition and product design. GP uses a stochastic search, tournament, and fitness function to explore a solution space. GP evolves a population of individual programs, through multiple generations, following the principals of biological evolution (mutation and reproduction) to discover a model that best fits or categorizes features in a given data set. We apply GP to categorization of LIGO noise and show that it can effectively be used to characterize the detector non-astrophysical noise both in low latency and offline searches.",
-
notes = "Abstract only, presentation online:
http://absuploads.aps.org/presentation.cfm?pid=12567
Abstract ID: BAPS.2017.APR.X6.8 National Science Foundation award PHY-1404139",
