Evolving random graph generators: A case for increased algorithmic primitive granularity

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Pope:2016:SSCI,
  author =       "Aaron S. Pope and Daniel R. Tauritz and 
                 Alexander D. Kent",
  booktitle =    "2016 IEEE Symposium Series on Computational
                 Intelligence (SSCI)",
  title =        "Evolving random graph generators: A case for increased
                 algorithmic primitive granularity",
  year =         "2016",
  abstract =     "Random graph generation techniques provide an
                 invaluable tool for studying graph related concepts.
                 Unfortunately, traditional random graph models tend to
                 produce artificial representations of real-world
                 phenomenon. Manually developing customized random graph
                 models for every application would require an
                 unreasonable amount of time and effort. In this work, a
                 platform is developed to automate the production of
                 random graph generators that are tailored to specific
                 applications. Elements of existing random graph
                 generation techniques are used to create a set of
                 graph-based primitive operations. A hyper-heuristic
                 approach is employed that uses genetic programming to
                 automatically construct random graph generators from
                 this set of operations. This work improves upon similar
                 research by increasing the level of algorithmic
                 sophistication possible with evolved solutions,
                 allowing more accurate modelling of subtle graph
                 characteristics. The versatility of this approach is
                 tested against existing methods and experimental
                 results demonstrate the potential to outperform
                 conventional and state of the art techniques for
                 specific applications.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SSCI.2016.7849929",
  month =        dec,
  notes =        "Also known as \cite{7849929}",
}

Genetic Programming entries for Aaron S Pope Daniel R Tauritz Alexander D Kent

Citations