Analysing the Genotype-Phenotype Map in Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{fagan:PhDThesis:2014,
  author =       "David Fagan",
  title =        "Analysing the Genotype-Phenotype Map in Grammatical
                 Evolution",
  school =       "University College Dublin",
  year =         "2013",
  address =      "Ireland",
  month =        "30 " # oct,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, piGE",
  URL =          "http://ncra.ucd.ie/papers/DavidFaganPhDThesis2014.pdf",
  size =         "207 pages",
  abstract =     "The Genotype-Phenotype Map (GPM) is an important
                 aspect of the representation in Evolutionary Computing
                 (EC). The GPM decouples the search space of the EC
                 algorithm into a many-to-one mapping, allowing an
                 abstraction of the search and solution spaces, which
                 can bring a number of benefits to search. Grammatical
                 Evolution (GE) is a grammar based form of Genetic
                 Programming (GP) that incorporates a GPM at its core,
                 which is loosely inspired by nature.

                 This thesis investigates whether different approaches
                 to the GPM can have a positive effect on GE's
                 performance. By examining a range of GPMs that use
                 differing expansion order principles it was found the
                 one approach, Position Independent Grammatical
                 Evolution (piGE) presented a viable alternative to the
                 canonical GE GPM.

                 piGE, while showing good performance, uses a variable
                 expansion order controlled by evolution. This variable
                 ordering increases the size of the search space that
                 must be navigated by piGE during evolution. It is found
                 that piGE gains a significant increase in connectivity
                 by using an evolvable order, while also providing piGE
                 with additional neutrality.

                 Knowing what orders piGE uses during evolution may
                 provide insight into new GPM approaches. With this in
                 mind a set of measures are devised, that allow for the
                 monitoring of piGE's population during an evolutionary
                 run. What is found is that piGE doesn't converge to a
                 single order but rather a distribution of GPM
                 orders.

                 The addition of the evolvable order in piGE provides an
                 added degree of freedom in the mapping that is not
                 exploited by standard genetic operations. A mutation
                 operation is presented that will allow the algorithm to
                 focus mutation on certain aspects of the piGE
                 chromosome. It is found that with this ability the
                 performance of piGE is increased.",
  notes =        "Supervisor: Michael O'Neill",
}

Genetic Programming entries for David Fagan

Citations