Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat

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

@PhdThesis{luke:dissertation,
  author =       "Sean Luke",
  title =        "Issues in Scaling Genetic Programming: Breeding
                 Strategies, Tree Generation, and Code Bloat",
  school =       "Department of Computer Science, University of
                 Maryland",
  address =      "A. V. Williams Building, University of Maryland,
                 College Park, MD 20742 USA",
  year =         "2000",
  size =         "178 pages",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.gmu.edu/~sean/papers/thesis2p.pdf",
  URL =          "http://www.cs.gmu.edu/~sean/papers/thesis2p.ps.gz",
  abstract =     "Genetic Programming is an evolutionary computation
                 technique which searches for those computer programs
                 that best solve a given problem. As genetic programming
                 is applied to increasingly difficult problems, its
                 effectiveness is hampered by the tendency of candidate
                 program solutions to grow in size independent of any
                 corresponding increases in quality. This bloat in
                 solutions slows the search process, interferes with
                 genetic programming's searching, and ultimately
                 consumes all available memory. The challenge for
                 scaling up genetic programming is to find the best
                 solutions possible before bloat puts a stop to
                 evolution. This can be tackled either by finding better
                 solutions more rapidly, or by taking measures to delay
                 bloat as long as possible.

                 This thesis discusses issues both in speeding the
                 search process and in delaying bloat in order to scale
                 genetic programming to tackle harder problems. It
                 describes evolutionary computation and genetic
                 programming, and details the application of genetic
                 programming to cooperative robot soccer and to language
                 induction. The thesis then compares genetic programming
                 breeding strategies, showing the conditions under which
                 each strategy produces better individuals with less
                 bloating. It then analyzes the tree growth properties
                 of the standard tree generation algorithms used, and
                 proposes new, fast algorithms which give the user
                 better control over tree size. Lastly, it presents
                 evidence which directly contradicts existing bloat
                 theories, and gives a more general theory of code
                 growth, showing that the issue is more complicated than
                 it first appears.",
  notes =        "errata 1. In Algorithm 2 (p. 6), the line P<-P\\[q]
                 should read P<-P\\[s].

                 2. Figures 5.2 through 5.5 (p. 38-39) are not in proper
                 evolutionary-time order. The proper order is 5.4, 5.5,
                 5.2, 5.3.",
}

Genetic Programming entries for Sean Luke

Citations