Decision support for complex planning challenges - Combining expert systems, engineering-oriented modeling, machine learning, information theory, and optimization technology

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Deschaine:thesis,
  author =       "Larry M. Deschaine",
  title =        "Decision support for complex planning challenges -
                 Combining expert systems, engineering-oriented
                 modeling, machine learning, information theory, and
                 optimization technology",
  school =       "Chalmers University of Technology",
  year =         "2014",
  series =       "Doktorsavhandlingar vid Chalmers tekniska hogskola. Ny
                 serie, No 3661",
  address =      "SE-412 96 Goteborg, Sweden",
  month =        "27 " # feb,
  keywords =     "genetic algorithms, genetic programming, Discipulus,
                 Decision analysis, model blending, model mixing, data
                 modelling engineering-oriented modelling, energy,
                 environmental, optimisation, analytic hierarchy
                 processes, machine learning, UXO, and MEC, land mines,
                 unexploded bombs, removal",
  isbn13 =       "978-91-7385-980-6",
  URL =          "http://publications.lib.chalmers.se/record/print-record/index.xsql?pubid=193490",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/Deschaine-Chalmers-PhD-Feb-2014.pdf",
  slides_url =   "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/LarryDeschaine-Chalmers-2-27-2014-FinalForDefence-EM-shared.pdf",
  size =         "233 pages",
  abstract =     "This thesis develops an approach for addressing
                 complex industrial planning challenges. The approach
                 provides advice to select and blend modelling
                 techniques that produce implementable optimal
                 solutions. Industrial applications demonstrate its
                 effectiveness. Industries have a need for advanced
                 analytic techniques that encompass and reconcile the
                 full range of information available regarding a
                 planning problem. The goal is to craft the best
                 possible decision in the time allotted. The pertinent
                 information can include subject matter expertise,
                 physical processes simulated in models, and
                 observational data. The approach described in this
                 paper assesses the decision challenge in two ways:
                 first according to the available knowledge profile
                 which includes the type, amount, and quality of
                 information available of the problem; and second,
                 according to the analysis and decision-support
                 techniques most appropriate to each profile. We use
                 model-mixing techniques such as machine learning and
                 Kalman Filtering to combine analysis methods from
                 various disciplines that include expert systems,
                 engineering-oriented numerical and symbolic modeling,
                 and machine learning in a graded, principled manner. A
                 suite of global and local optimisation methods handle
                 the range of optimization tasks arising in the
                 demonstrated engineering projects. The methods used
                 include the global and local nonlinear optimization
                 algorithms. The thesis consists of four appended
                 papers. Paper I uses subject matter expertise modelling
                 to provide decision analysis regarding the
                 environmental issue of mercury retirement. Paper II
                 provides the framework for developing optimal
                 remediation designs for subsurface groundwater
                 monitoring and contamination mitigation using numerical
                 models based on physical understanding. Paper III
                 provides the results of a machine learning study using
                 the Compiling Genetic Programming System (CGPS) on
                 multiple industrial data sets. This study resulted in a
                 breakthrough for identifying underground unexploded
                 ordnance (UXO) and munitions and explosives of concern
                 (MEC) from inert buried objects. Paper IV develops and
                 uses the model mixing and optimisation approach to
                 expound on understanding the MEC identification
                 technique. It uses the methods in the first three
                 papers along with additional technology. Each thesis
                 paper includes complimentary citations and web links to
                 selected publications that further demonstrate the
                 value of this approach; either via industrial
                 application or inclusion in US government guidance
                 documents.",
  notes =        "Winner the top prize in the research category
                 competition at the American Academy of Environmental
                 Engineers and Scientists (aaees.org). Heavily based on
                 thesis. 'Physics-Based Management Optimization
                 Technology for Supporting Environmental and Water
                 Resource Management' HydroGeoLogic, Inc.
                 http://www.aaees.org/e3scompetition-winners-2017gp-research.php
                 PBMO

                 In English. Number 193490

                 Supervisor: Peter Nordin",
}

Genetic Programming entries for Larry M Deschain

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