Using multi-objective genetic programming to evolve stochastic logic gate circuits

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

@InProceedings{Ross:2015:ieeeCIBCB,
  author =       "Brian J. Ross",
  booktitle =    "2015 IEEE Conference on Computational Intelligence in
                 Bioinformatics and Computational Biology (CIBCB)",
  title =        "Using multi-objective genetic programming to evolve
                 stochastic logic gate circuits",
  year =         "2015",
  abstract =     "A new stochastic logic gate language is presented.
                 Blossey et al.'s stochastic gene gate language is
                 extended with a complete set of stochastic Boolean
                 gates. Although the gates have behavioural similarities
                 to conventional logic gates, a major difference is that
                 they operate on quantities of products or substances
                 that dynamically vary over time. A gene gate circuit's
                 behaviour is characterised by a time-course plot of the
                 substance quantities. The paper studies the Boolean
                 gate language by using multi-objective genetic
                 programming to evolve logic gate circuits that conform
                 to a number of different target systems. Circuit
                 behaviour is characterised by sets of up to 15 time
                 course statistics, and sum of ranks is used as a
                 many-objective scoring strategy. Results show that the
                 language is highly compositional, just like
                 conventional logic expressions, and that multiple
                 circuits can exhibit similar behaviours. The new gate
                 language uses Blossey et al.'s gates as a rudimentary
                 basis within evolved circuits, with the advantage of
                 using higher-level Boolean gates when necessary. The
                 identification of candidate solutions can be
                 challenging, however, and must account for noise
                 inherent in the time course behaviours. Circuit
                 behaviour is also highly dependent on channel rates,
                 and future work applying the language to real-world
                 data will need to address this sensitivity.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CIBCB.2015.7300283",
  month =        aug,
  notes =        "Dept. of Comput. Sci., Brock Univ., St. Catharines,
                 ON, Canada

                 Also known as \cite{7300283}",
}

Genetic Programming entries for Brian J Ross

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