Flow of Control in Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Schonfeld:2015:CEC,
  author =       "Justin Schonfeld and Daniel Ashlock",
  title =        "Flow of Control in Linear Genetic Programming",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "1175--1182",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://eldar.mathstat.uoguelph.ca/dashlock/eprints/AFC.pdf",
  DOI =          "doi:10.1109/CEC.2015.7257022",
  abstract =     "Traditional flow of control for linear genetic
                 programming includes structures such as if-then-else
                 statements combined with gotos. In this study we
                 examine additional classes of flow of control
                 structures. The first is called the alternator. This is
                 a deterministically variable flow of control that
                 executes a goto every other time it is accessed. We
                 demonstrate that evolution can use alternators that
                 jump past one another to create solutions with
                 significantly more complexity than those created by
                 solutions without alternators for a simple binary
                 string generation problem. The alternator, while
                 clearly useful, would be difficult for human
                 programmers to use effectively. The alternator thus
                 demonstrates a strong disjunction between
                 human-friendly and evolution-friendly programming
                 languages. Domain specific flow of control structures
                 tailored to the environment being studied are also
                 examined. These are statements carefully designed for
                 the problems being solved. Allowing controllers solving
                 the Tartarus task to change the flow of control based
                 on knowledge of their position in the interior boundary
                 of a world substantially enhances the performance of
                 the controllers. Comparison of the three different
                 fitness functions used demonstrates that the benefit of
                 the alternate flow-of-control is domain specific.",
  notes =        "1355 hrs 15464 CEC2015",
}

Genetic Programming entries for Justin Schonfeld Daniel Ashlock

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