Automatic control program creation using concurrent Evolutionary Computing

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{hart:thesis,
  author =       "John K. Hart",
  title =        "Automatic control program creation using concurrent
                 Evolutionary Computing",
  school =       "Bournemouth University",
  year =         "2004",
  address =      "UK",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://eprints.bournemouth.ac.uk/394/",
  URL =          "http://eprints.bournemouth.ac.uk/394/1/John_Hart.pdf",
  size =         "112 pages",
  abstract =     "Over the past decade, Genetic Programming (GP) has
                 been the subject of a significant amount of research,
                 but this has resulted in the solution of few complex
                 real-world problems. In this work, I propose that, for
                 some relatively simple, non safety -critical embedded
                 control applications, GP can be used as a practical
                 alternative to software developed by humans.

                 Embedded control software has become a branch of
                 software engineering with distinct temporal, interface
                 and resource constraints and requirements. This results
                 in a characteristic software structure, and by
                 examining this, the effective decomposition of an
                 overall problem into a number of smaller, simpler
                 problems is performed. It is this type of problem
                 amelioration that is suggested as a method whereby
                 certain real -world problems may be rendered into a
                 soluble form suitable for GP.

                 In the course of this research, the body of published
                 GP literature was examined and the most important
                 changes to the original GP technique of Koza are noted;
                 particular focus is made upon GP techniques involving
                 an element of concurrency -which is central to this
                 work. This search highlighted few applications of GP
                 for the creation of software for complex, realworld
                 problems -this was especially true in the case of multi
                 thread, multi output solutions.

                 To demonstrate this Idea, a concurrent Linear GP (LGP)
                 system was built that creates a multiple input
                 -multiple output solution using a custom low -level
                 evolutionary language set, combining both continuous
                 and Boolean data types. The system uses a multi
                 -tasking model to evolve and execute the required LGP
                 code for each system output using separate populations:
                 Two example problems -a simple fridge controller and a
                 more complex washing machine controller are described,
                 and the problems encountered and overcome during the
                 successful solution of these problems, are detailed.
                 The operation of the complete, evolved washing machine
                 controller is simulated using a graphical LabVIEW
                 application.

                 The aim of this research is to propose a general
                 purpose system for the automatic creation of control
                 software for use in a range of problems from the target
                 problem class -without requiring any system tuning: In
                 order to assess the system search performance
                 sensitivity, experiments were performed using various
                 population and LGP string sizes; the experimental data
                 collected was also used to examine the utility of
                 abandoning stalled searches and restarting. This work
                 is significant because it identifies a realistic
                 application of GP that can ease the burden of finite
                 human software design resources, whilst capitalising on
                 accelerating computing potential.",
  notes =        "related publications \cite{hart:2002:gecco:lbp}
                 \cite{hart:2004:eurogp}

                 Supervisor Martin Shepperd (1st) and Martin Lefley
                 (2nd)",
}

Genetic Programming entries for John Hart

Citations