On genetic algorithms and Lindenmayer systems

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@InProceedings{Ochoa:1998:PPSN,
  author =       "Gabriela Ochoa",
  title =        "On genetic algorithms and Lindenmayer systems",
  booktitle =    "Parallel Problem Solving from Nature, PPSN V",
  year =         "1998",
  editor =       "Agoston E. Eiben and Thomas Baeck and 
                 Marc Schoenauer and Hans-Paul Schwefel",
  volume =       "1498",
  series =       "Lecture Notes in Computer Science",
  pages =        "335--344",
  address =      "Amsterdam",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-65078-2",
  URL =          "http://www.cs.stir.ac.uk/~goc/papers/GAsandL-systems.pdf",
  DOI =          "doi:10.1007/BFb0056876",
  size =         "10 pages",
  abstract =     "This paper describes a system for simulating the
                 evolution of artificial 2D plant morphologies. Virtual
                 plant genotypes are inspired by the mathematical
                 formalism known as Lindenmayer systems (L-systems). The
                 phenotypes are the branching structures resulting from
                 the derivation and graphic interpretation of the
                 genotypes. Evolution is simulated using a genetic
                 algorithm with a fitness function inspired by current
                 evolutionary hypotheses concerning the factors that
                 have had the greatest effect on plant evolution. The
                 system also provides interactive selection, allowing
                 the user to direct simulated evolution towards
                 preferred phenotypes. Simulation results demonstrate
                 many interesting structures, suggesting that artificial
                 evolution constitutes a powerful tool for (1) exploring
                 the large, complex space of branching structures found
                 in nature, and (2) generating novel ones. Finally, we
                 emphasise that Lindenmayer systems constitute a highly
                 suitable encoding for artificial evolution studies.",
}

Genetic Programming entries for Gabriela Ochoa

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