Assisting Asset Model Development with Evolutionary Augmentation

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Steven Gustafson and Arun Subramaniyan and 
                 Aisha Yousuf",
  title =        "Assisting Asset Model Development with Evolutionary
  booktitle =    "Genetic Programming Theory and Practice XIV",
  year =         "2016",
  editor =       "Rick Riolo and Bill Worzel and Brian Goldman and 
                 Bill Tozier",
  address =      "Ann Arbor, USA",
  month =        "19-21 " # may,
  publisher =    "Springer",
  note =         "Forthcoming",
  keywords =     "genetic algorithms, genetic programming, genetic
                 programming, lifting models, machine learning,
                 industrial applications, real-world application,
                 knowledge capture, artificial intelligence, intelligent
  isbn13 =       "978-3-319-97087-5",
  URL =          "",
  URL =          "",
  abstract =     "In this chapter, we explore how Genetic Programming
                 can assist and augment the expert-driven process of
                 developing data-driven models. In our use case,
                 modellers must develop hundreds of models that
                 represent individual properties of a part, components,
                 assets, systems and meta-systems like a power plant.
                 Each of these models is developed with an objective in
                 mind, like estimating the useful remaining life or
                 anomaly detection. As such, the modeller uses their
                 expert judgement as well as available data to select
                 the most appropriate method. In this initial paper, we
                 examine the most basic example of when the expert
                 selects a kind of regression modelling approach and
                 develops a model from data. We then use that captured
                 domain knowledge from their process as well as end
                 model to determine if Genetic Programming can augment,
                 assist and improve their final result. We show that
                 while Genetic Programming can indeed find improved
                 solutions according to an error metric, it is much
                 harder for Genetic Programming to find models that do
                 not increase complexity. Also, we find that one
                 approach in particular shows promise as a way to
                 incorporate domain knowledge.",
  notes =        "

                 Part of \cite{Tozier:2016:GPTP} to be published after
                 the workshop",

Genetic Programming entries for Steven M Gustafson Arun Subramaniyan Aisha Yousuf