Evolving Efficient Solutions to Complex Problems Using the Artificial Epigenetic Network

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@InProceedings{turner2015evolving,
  author =       "Alexander P. Turner and Martin A. Trefzer and 
                 Michael A. Lones and Andy M. Tyrrell",
  title =        "Evolving Efficient Solutions to Complex Problems Using
                 the Artificial Epigenetic Network",
  booktitle =    "10th International Conference on Information
                 Processing in Cells and Tissues, IPCAT 2015",
  year =         "2015",
  editor =       "Michael Lones and Andy Tyrrell and Stephen Smith and 
                 Gary Fogel",
  volume =       "9303",
  series =       "LNCS",
  address =      "San Diego, CA, USA",
  month =        sep # " 14-16",
  publisher =    "Springer International Publishing",
  pages =        "153--165",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-319-23108-2_13",
  abstract =     "The artificial epigenetic network (AEN) is a
                 computational model which is able to topologically
                 modify its structure according to environmental
                 stimulus. This approach is inspired by the
                 functionality of epigenetics in nature, specifically,
                 processes such as chromatin modifications which are
                 able to dynamically modify the topology of gene
                 regulatory networks. The AEN has previously been shown
                 to perform well when applied to tasks which require a
                 range of dynamical behaviours to be solved optimally.
                 In addition, it has been shown that pruning of the AEN
                 to remove non-functional elements can result in highly
                 compact solutions to complex dynamical tasks. In this
                 work, a method has been developed which provides the
                 AEN with the ability to self prune throughout the
                 optimisation process, whilst maintaining functionality.
                 To test this hypothesis, the AEN is applied to a range
                 of dynamical tasks and the most optimal solutions are
                 analysed in terms of function and structure.",
  notes =        "Affiliated with Department of Electronics, University
                 of York",
}

Genetic Programming entries for Alexander P Turner Martin A Trefzer Michael A Lones Andrew M Tyrrell

Citations