Towards Automatic Controller Design using Multi-Objective Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@PhdThesis{Pedersen:thesis,
  author =       "Gerulf K. M. Pedersen",
  title =        "Towards Automatic Controller Design using
                 Multi-Objective Evolutionary Algorithms",
  school =       "Department of Control Engineering, Aalborg
                 University",
  year =         "2005",
  address =      "Fredrik Bajers Vej 7C, DK-9220 Aalborg, Denmark",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://vbn.aau.dk/en/publications/towards-automatic-controller-design-using-multiobjective-evolutionary-algorithms%284c103100-f596-11da-9980-000ea68e967b%29.html",
  URL =          "http://vbn.aau.dk/files/501667/thesis.pdf",
  ISBN =         "8790664272",
  size =         "190 pages",
  abstract =     "In order to design the controllers of tomorrow, a need
                 has risen for tools that can aid in the design of
                 these. A desire to use evolutionary computation as a
                 tool to achieve that goal is what gave inspiration for
                 the work contained in this thesis. After having studied
                 the foundations of evolutionary computation, a choice
                 was made to use multi-objective algorithms for the
                 purpose of aiding in automatic controller design. More
                 specifically, the choice was made to use the
                 Non-dominated Sorting Genetic Algorithm II (NSGA-II),
                 which is one of the most potent algorithms currently in
                 use, as the foundation for achieving the desired
                 goal.

                 While working with the algorithm, some issues arose
                 which limited the use of the algorithm for unknown
                 problems. These issues included the relative scale of
                 the used fitness functions and the distribution of
                 solutions on the optimal Pareto front. Some work has
                 previously been done in this area using methods based
                 on relative angles, utility functions, and projections
                 and that work is what is extended in this thesis in
                 order to cover a wider range of problems. This allows
                 the NSGA-II to be transformed into a 'black-box'
                 optimisation tool, which can be used for automatic
                 controller design.

                 However, because the field of evolutionary computation
                 is relatively unknown in the field of control
                 engineering, this thesis also includes a comprehensive
                 introduction to the basic field of evolutionary
                 computation as well as a description of how the field
                 has previously been used for solving a variety of
                 issues in control engineering.",
  notes =        "Some mention of genetic programming, particularly in
                 chapters 2 and 3.",
}

Genetic Programming entries for Gerulf K M Pedersen