Multi-Objective Optimisation of Cell-Array Circuit Evolution

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

@InProceedings{Bremner:2011:MOoCCE,
  title =        "Multi-Objective Optimisation of Cell-Array Circuit
                 Evolution",
  author =       "Paul Bremner and Mohammad Samie and Anthony Pipe and 
                 Andy Tyrrell",
  pages =        "440--446",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
                 Computation",
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, cell-array circuit evolution,
                 circuit decomposition technique, custom FPGA, digital
                 circuit, interconnected configurable cell,
                 multiobjective optimisation, field programmable gate
                 arrays",
  DOI =          "doi:10.1109/CEC.2011.5949651",
  abstract =     "In this paper we have investigated the efficacy of
                 applying multi-objective optimisation to Cartesian
                 genetic programming (CGP) when used for evolution of
                 cell-array configurations. A cell-array is a proposed
                 type of custom FPGA, where digital circuits can be
                 formed from interconnected configurable cells; thus,
                 the CGP nodes are more complex than in its standard
                 implementation. We have described modifications to a
                 previously described optimisation algorithm that has
                 led to significant improvements in performance;
                 circuits close to a hand designed equivalent have been
                 found, in terms of the optimised objectives.
                 Additionally we have investigated the effect of circuit
                 decomposition techniques on evolutionary performance.
                 We found that using a hybrid of input and output
                 decomposition techniques substantial reductions in
                 evolution time were observed. Further, while the number
                 of circuit inputs is the key factor for functional
                 evolution time, the number of circuit outputs is the
                 key factor for optimisation time.",
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",
}

Genetic Programming entries for Paul Bremner Mohammad Samie Anthony Pipe Andrew M Tyrrell

Citations