Constituent Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{Georgiou:thesis,
  author =       "Loukas Georgiou",
  title =        "Constituent Grammatical Evolution",
  school =       "School of Computer Science, Bangor University",
  year =         "2012",
  address =      "LL57 1UT, Gwynedd, UK",
  month =        aug,
  email =        "loukas.georgiou@gmail.com",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, artificial ant, maze search, grammatical
                 bias, modularity",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Georgiou_thesis.pdf",
  size =         "281 pages",
  abstract =     "Evolutionary algorithms are a competent
                 nature-inspired approach for complex computational
                 problem solving. One recent development is Grammatical
                 Evolution, a grammar-based evolutionary algorithm which
                 uses genotypes of variable length binary strings and a
                 unique genotype-to-phenotype mapping process based on a
                 BNF grammar definition describing the output language
                 that is able to create valid individuals of an
                 arbitrary structure or programming language.

                 This study surveys Grammatical Evolution, identifies
                 its most important issues, investigates the competence
                 of the algorithm in a series of agent-oriented
                 benchmark problems, provides experimental results which
                 cast doubt about its effectiveness and efficiency on
                 problems involving the evolution of the behaviour of an
                 agent, and presents Constituent Grammatical Evolution
                 (CGE), a new innovative evolutionary automatic
                 programming algorithm. CGE extends Grammatical
                 Evolution by incorporating the concepts of constituent
                 genes and conditional behaviour-switching. It builds
                 from elementary and more complex building blocks a
                 control program which dictates the behaviour of an
                 agent and it is applicable to the class of problems
                 where the subject of search is the behaviour of an
                 agent in a given environment. Experimental results show
                 that the new algorithm significantly improves
                 Grammatical Evolution in all problems it has been
                 benchmarked.

                 Additionally, the investigation undertaken in this work
                 required the development of a series of tools which are
                 presented and described in detail. These tools provide
                 an extendable open source and publicly available
                 framework for experimentation in the area of
                 evolutionary algorithms and their application in
                 agent-oriented environments and complex systems.",
  notes =        "jGE CGE NetLogo

                 Supervisor: William J. Teahan",
}

Genetic Programming entries for Loukas Georgiou

Citations