Tuning Genetic Programming Performance via Bloating Control and a Dynamic Fitness Function Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Geng Li",
  title =        "Tuning Genetic Programming Performance via Bloating
                 Control and a Dynamic Fitness Function Approach",
  school =       "Computer Science, University of Manchester",
  year =         "2013",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-scw:211199&datastreamId=FULL-TEXT.PDF",
  URL =          "https://www.escholar.manchester.ac.uk/uk-ac-man-scw:211199",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=58&uin=uk.bl.ethos.607007",
  size =         "171 pages",
  abstract =     "Inspired by Darwin's natural selection, genetic
                 programming is an evolutionary computation technique
                 which searches for computer programs best solving an
                 optimisation problem. The ability of GP to perform
                 structural optimization at the same time of parameter
                 optimisation makes it uniquely suitable to solve more
                 complex optimisation problems, in which the structure
                 of the solution is not known a priori. But, as GP is
                 applied to increasingly difficult problems, the
                 efficiency of the algorithm has been severely limited
                 by bloating. Previous studies of bloating suggest that
                 bloating can be resolved either directly by delaying
                 the growth in depth and size, or indirectly by making
                 GP to find optimal solutions faster. This thesis
                 explores both options in order to improve the
                 scalability and the capacity of GP algorithm. It
                 tackles the former by firstly systematically analysing
                 the effect of bloating using a mathematical tool
                 developed called activation rate. It then proposes
                 depth difference hypothesis as a new cause of bloating
                 and investigates depth constraint crossover as a new
                 bloating control method, which is able to give very
                 competitive control over bloating without affecting the
                 exploration of fitter individuals. This thesis explores
                 the second option by developing norm-referenced fitness
                 function, which dynamically determines the individual's
                 fitness based on not only how well it performs, but
                 also the population's average performance as well. It
                 is shown both theoretically and empirically that,
                 norm-referenced fitness is able to significantly
                 improve GP performance over the standard GP setup.",
  notes =        "Supervisor: Xiao-Jun Zeng uk.bl.ethos.607007
                 Manchester eScholar ID: uk-ac-man-scw:211199",

Genetic Programming entries for Geng Li