Learning Benefits Evolution if Sex Gives Pleasure

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "A. R. Griffioen and S. K. Smit and A. E. Eiben",
  title =        "Learning Benefits Evolution if Sex Gives Pleasure",
  booktitle =    "2008 IEEE World Congress on Computational
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "2073--2080",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0492.pdf",
  URL =          "http://www.cs.vu.nl/~gusz/papers/2008-CEC-Griffioen-Smit-Eiben.pdf",
  DOI =          "doi:10.1109/CEC.2008.4631073",
  abstract =     "In this paper the effects of individual learning on an
                 evolving population of situated agents are
                 investigated. We work with a novel type of system where
                 agents can decide autonomously (by their controllers)
                 if/when they reproduce and the bias in the agent
                 controllers for the mating action is adaptable by
                 individual learning. Our experiments show that in such
                 a system reinforcement learning with the
                 straightforward rewards system based on energy makes
                 the agents lose their interest in mating. In other
                 words, we see that learning frustrates evolution,
                 killing the whole population on the long run. This
                 effect can be counteracted by introducing a specially
                 designated positive mating reward, pretty much like an
                 orgasm in Nature.With this twist individual learning
                 becomes a positive force. It can make the otherwise
                 disappearing population viable by keeping agents alive
                 that did not yet learn the task at hand. This hiding
                 effect proves positive for it provides a smooth road
                 for the population to adapt and learn the task with a
                 lower risk of extinction.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",

Genetic Programming entries for Robert Griffioen Selmar Kagiso Smit Gusz Eiben