Population Variation in Canonical Tree-based Genetic Programming

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

@PhdThesis{Kouchakpour:thesis,
  author =       "Peyman Kouchakpour",
  title =        "Population Variation in Canonical Tree-based Genetic
                 Programming",
  school =       "School of Electrical, Electronic and Computer
                 Engineering, University of Western Australia",
  year =         "2008",
  address =      "Perth",
  month =        May,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://robotics.ee.uwa.edu.au/theses/2008-Genetic-Kouchakpour-PhD.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.9061",
  size =         "284 pages",
  abstract =     "The Genetic Programming paradigm, which applies the
                 Darwinian principle of evolution to hierarchical
                 computer programs, has produced promising breakthroughs
                 in various scientific and engineering applications.
                 However, one of the main drawbacks of Genetic
                 Programming has been the often large amount of
                 computational effort required to solve complex
                 problems. There have been various amounts of research
                 conducted to devise innovative methods to improve the
                 efficiency of Genetic Programming. This thesis has
                 three main contributions. It firstly provides a
                 comprehensive overview of the related work to improve
                 the performance of Genetic Programming and classifies
                 these various proposed approaches into categories.
                 Secondly, a new static population variation scheme (PV)
                 is proposed, whereby the size of the population is
                 varied according to a predetermined schedule during the
                 execution of the Genetic Programming system with the
                 aim of reducing the computational effort with respect
                 to that of Standard Genetic Programming. Within this
                 new static scheme the initial population size is made
                 to be different from the initial size of the Standard
                 Genetic Programming such that the worst case
                 computational effort is never greater than that of the
                 Standard Genetic Programming. Various static schemes
                 for altering population size under this proposal are
                 investigated using a comprehensive range of standard
                 problems to determine whether the nature of the
                 'population variation', i.e. the way the population is
                 varied during the search, has any significant impact on
                 Genetic Programming performance. It is shown that these
                 population variation schemes do have the capacity to
                 provide solutions at a lower computational cost
                 compared with the Standard Genetic Programming.
                 Thirdly, three innovations for dynamically varying the
                 population size during the run of the Genetic
                 Programming system are proposed. These are related to
                 what is called Dynamic Population Variation (DPV),
                 where the size of the population is dynamically varied
                 using a heuristic feedback mechanism during the
                 execution of the Genetic Programming with the aim of
                 reducing the computational effort. The efficacy of
                 these innovations is examined using the same
                 comprehensive range of standard representative
                 problems. It is shown that these new ideas do have the
                 capacity to provide solutions at a lower computational
                 cost compared with standard genetic programming and
                 previously reported algorithms. Finally, further
                 interesting research potentials for population
                 variation are identified together with some of the open
                 areas of research within the Genetic Programming and
                 also possible future trends in this discipline.",
}

Genetic Programming entries for Peyman Kouchakpour

Citations