Repeated Sequences in Linear Genetic Programming Genomes

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

@Article{langdon:2005:CS,
  author =       "William B. Langdon and Wolfgang Banzhaf",
  title =        "Repeated Sequences in Linear Genetic Programming
                 Genomes",
  journal =      "Complex Systems",
  year =         "2005",
  volume =       "15",
  number =       "4",
  pages =        "285--306",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, artificial evolution, frequent patterns,
                 repeated sequences, hierarchical building blocks,
                 repetitive elements, microsatellites, unequal
                 crossover, duplication tandemly repeated genes, growth
                 of genomes, repeats finder, SSR tracts, GPengine,
                 Discipulus",
  ISSN =         "0891-2513",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_repeat_linear.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_repeat_linear.ps.gz",
  URL =          "http://www.complex-systems.com/pdf/15-4-2.pdf",
  URL =          "http://www.complex-systems.com/abstracts/v15_i04_a02.html",
  size =         "22 pages",
  abstract =     "Biological chromosomes are replete with repetitive
                 sequences, microsatellites, SSR tracts, ALU, etc. in
                 their DNA base sequences. We started looking for
                 similar phenomena in evolutionary computation. First
                 studies find copious repeated sequences, which can be
                 hierarchically decomposed into shorter sequences, in
                 programs evolved using both homologous and two point
                 crossover but not with headless chicken crossover or
                 other mutations. In bloated programs the small number
                 of effective or expressed instructions appear in both
                 repeated and non-repeated code. Hinting that
                 building-blocks or code reuse may evolve in unplanned
                 ways.

                 Mackey-Glass chaotic time series prediction and
                 eukaryotic protein localisation (both previously used
                 as artificial intelligence machine learning benchmarks)
                 demonstrate evolution of Shannon information (entropy)
                 and lead to models capable of lossy Kolmogorov
                 compression. Our findings with diverse benchmarks and
                 GP systems suggest this emergent phenomenon may be
                 widespread in genetic systems.",
  notes =        "Extended version of \cite{langdon:2004:geccolb}

                 ",
}

Genetic Programming entries for William B Langdon Wolfgang Banzhaf

Citations