An Analysis of Diversity in Genetic Programming

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

@PhdThesis{gustafson:2004:phdthesis,
  author =       "Steven Gustafson",
  title =        "An Analysis of Diversity in Genetic Programming",
  school =       "School of Computer Science and Information Technology,
                 University of Nottingham",
  year =         "2004",
  month =        feb,
  address =      "Nottingham, England",
  keywords =     "genetic algorithms, genetic programming",
  size =         "170 pages",
  URL =          "http://www.cs.nott.ac.uk/~smg/research/publications/phdthesis-gustafson.pdf",
  URL =          "http://www.cs.nott.ac.uk/~smg/research/publications/phdthesis-gustafson.ps",
  URL =          "http://www.cs.nott.ac.uk/~smg/research/publications/phdthesis-gustafson.ps.gz",
  URL =          "http://www.gustafsonresearch.com/thesis_html/",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=6&uin=uk.bl.ethos.404029",
  abstract =     "Genetic programming is a metaheuristic search method
                 that uses a population of variable-length computer
                 programs and a search strategy based on biological
                 evolution. The idea of automatic programming has long
                 been a goal of artificial intelligence, and genetic
                 programming presents an intuitive method for
                 automatically evolving programs. However, this method
                 is not without some potential drawbacks. Search using
                 procedural representations can be complex and
                 inefficient. In addition, variable sized solutions can
                 become unnecessarily large and difficult to
                 interpret.

                 The goal of this thesis is to understand the dynamics
                 of genetic programming that encourages efficient and
                 effective search. Toward this goal, the research
                 focuses on an important property of genetic programming
                 search: the population. The population is related to
                 many key aspects of the genetic programming algorithm.
                 In this programme of research, diversity is used to
                 describe and analyse populations and their effect on
                 search. A series of empirical investigations are
                 carried out to better understand the genetic
                 programming algorithm.

                 the relationship between diversity and search. The
                 effect of increased population diversity and a metaphor
                 of search are then examined. This is followed by an
                 investigation into the phenomenon of increased solution
                 size and problem difficulty. The research concludes by
                 examining the role of diverse individuals, particularly
                 the ability of diverse individuals to affect the search
                 process and ways of improving the genetic programming
                 algorithm.

                 (1) An analysis shows the complexity of the issues of
                 diversity and the relationship between diversity and
                 fitness, (2) The genetic programming search process is
                 characterised by using the concept of genetic lineages
                 and the sampling of structures and behaviours, (3) A
                 causal model of the varied rates of solution size
                 increase is presented, (4) A new, tunable problem
                 demonstrates the contribution of different population
                 members during search, and (5) An island model is
                 proposed to improve the search by speciating dissimilar
                 individuals into better-suited environments.

                 Currently, genetic programming is applied to a wide
                 range of problems under many varied contexts. From
                 artificial intelligence to operations research, the
                 results presented in this thesis will benefit
                 population-based search methods, methods based on the
                 concepts of evolution and search methods using
                 variable-length representations.",
}

notes = {uk.bl.ethos.404029},

Genetic Programming entries for Steven M Gustafson

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