Cartesian Genetic Programming for Control Engineering

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Tim Clarke",
  title =        "Cartesian Genetic Programming for Control
  booktitle =    "Inspired by Nature: Essays Presented to Julian F.
                 Miller on the Occasion of his 60th Birthday",
  publisher =    "Springer",
  year =         "2017",
  editor =       "Susan Stepney and Andrew Adamatzky",
  volume =       "28",
  series =       "Emergence, Complexity and Computation",
  chapter =      "7",
  pages =        "157--173",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming",
  isbn13 =       "978-3-319-67996-9",
  DOI =          "doi:10.1007/978-3-319-67997-6_7",
  abstract =     "Genetic programming has a proven ability to discover
                 novel solutions to engineering problems. The author has
                 worked with Julian F. Miller, together with some
                 undergraduate and postgraduate students, over the last
                 ten or so years in exploring innovation through
                 evolution, using Cartesian Genetic Programming (CGP).
                 Our co-supervisions and private meetings stimulated
                 many discussions about its application to a specific
                 problem domain: control engineering. Initially, we
                 explored the design of a flight control system for a
                 single rotor helicopter, where the author has
                 considerable theoretical and practical experience. The
                 challenge of taming helicopter dynamics (which are
                 non-linear, highly cross-coupled and unstable) seemed
                 ideally suited to the application of CGP. However, our
                 combined energies drew us towards the more fundamental
                 issues of how best to generalise the problem with the
                 objective of freeing up the innovation process from
                 constrictions imposed by conventional engineering
                 thinking. This chapter provides an outline of our
                 thoughts and hopefully may motivate a reader out there
                 to progress this still embryonic research. The scene is
                 set by considering a simple class of problems: the
                 single-input, single-output, linear, time-invariant
  notes =        "part of \cite{miller60book}

Genetic Programming entries for Tim Clarke