Genetic Programming and Co-Evolution with Exogenous Fitness in an Artificial Life Environment

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Michael Waters and John Sheppard",
  title =        "Genetic Programming and Co-Evolution with Exogenous
                 Fitness in an Artificial Life Environment",
  booktitle =    "Proceedings of the Congress on Evolutionary
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "3",
  pages =        "1641--1648",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, coevolution,
                 AI Wars, artificial life environment, artificial
                 organisms, co-evolution, commercially available
                 environment, decision processes, endogenous fitness,
                 evolutionary computation, evolutionary performance,
                 exogenous fitness, fitness factors, fitness function,
                 fitness landscape, hostile environment, artificial
                 life, competitive algorithms, decision theory",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "",
  DOI =          "doi:10.1109/CEC.1999.785471",
  abstract =     "The study of artificial life involves simulating
                 biological or sociological processes with a computer.
                 Combining artificial life with techniques from
                 evolutionary computation frequently involves modelling
                 the behaviour or decision processes of artificial
                 organisms within a society in such a way that genetic
                 algorithms can be applied to modify these models and
                 enhance behavior over time. Typically, endogenous
                 fitness is used with co-evolution. We explore the use
                 of an exogenous fitness function with genetic
                 programming and co-evolution to develop individuals and
                 species capable of competing in a hostile environment.
                 To facilitate the study, we use a commercially
                 available environment-AI Wars-to host the organisms and
                 run the experiments. Results from our experiments,
                 though preliminary, indicate the ability of
                 coevolution, genetic programming, and exogenous fitness
                 to evolve fit individuals. The results also suggest the
                 ability to assess the nature of the fitness landscape
                 and the impact of various fitness factors on
                 evolutionary performance",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",

Genetic Programming entries for Michael Waters John W Sheppard