Reducing Premature Convergence in Evolutionary Algorithms

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

@PhdThesis{ryan:thesis,
  author =       "Conor Ryan",
  title =        "Reducing Premature Convergence in Evolutionary
                 Algorithms",
  school =       "University College, Cork",
  year =         "1996",
  address =      "Ireland",
  month =        "2 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  broken =       "ftp://odyssey.ucc.ie/pub/genetic/thesis.ps.Z",
  URL =          "http://citeseer.ist.psu.edu/cache/papers/cs/6401/ftp:zSzzSzodyssey.ucc.iezSzpubzSzgeneticzSzthesis.pdf/ryan96reducing.pdf",
  URL =          "http://citeseer.ist.psu.edu/185331.html",
  size =         "138 pages",
  abstract =     "

                 We define Evolutionary Algorithms to be those
                 algorithms which employ or model natural evolution.
                 Generally, when an Evolutionary Algorithm fails to
                 produce a satisfactory solution to a problem, it is
                 because the population has prematurely converged to a
                 suboptimal solution. This thesis seeks to improve the
                 performance of Evolutionary Algorithms by reducing the
                 occurrence of premature convergence. All the extensions
                 presented in this thesis are either naturally occurring
                 phenomena, or are methods employed by biologists and /
                 or plant and animal breeders. In all the cases examined
                 in this thesis, it is shown that the less human control
                 there is with evolution, the better a population will
                 perform. A number of standard benchmark problems are
                 examined, and new, biologically-inspired approaches are
                 presented. A new selection scheme involving multiple
                 fitness functions is introduced. This scheme is applied
                 to the optimisation of multi-objective functions and
                 multi-modal functions. Genetic Programming is applied
                 to a new problem area, the autoparallelisation of
                 serial programs, through the use of techniques
                 developed in this thesis. The notion of addditive
                 diploidy, a type of diploidy that occurs naturally in
                 biology, is introduced and applied to Genetic
                 Algorithms. Additive diploidy is shown to outperform
                 traditional, dominance-oriented, diploidy on a
                 difficult test problem. A new benchmark problem for
                 Genetic Programming is introduced. This
                 competition-oriented benchmark permits the direct
                 comparison of two or more possible solutions. In
                 producing individuals for this benchmark, Genetic
                 Programming is also shown to be suitable for the
                 evolution of event driven programs.",
  notes =        "

                 It is general insofar as it covers several EAs, but the
                 algorithm that gets the most coverage is GP.",
}

Genetic Programming entries for Conor Ryan