Multi-objective optimization of QCA circuits with multiple outputs using genetic programming

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

@Article{Rezaee:2013:GPEM,
  author =       "Razieh Rezaee and Mahboobeh Houshmand and 
                 Monireh Houshmand",
  title =        "Multi-objective optimization of QCA circuits with
                 multiple outputs using genetic programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2013",
  volume =       "14",
  number =       "1",
  pages =        "95--118",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Hardware
                 reduction, Delay reduction, Majority expression,
                 Multi-output circuits, Multi-objective GP",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-012-9173-6",
  size =         "24 pages",
  abstract =     "Quantum-Dot Cellular Automata (QCA) is a promising
                 nanotechnology that has been recognised as one of the
                 top emerging technologies in future computers. Size
                 density of several orders of magnitude smaller than
                 Complementary Metal-Oxide Semiconductor, fast switching
                 time and extremely low power, has caused QCA to become
                 a topic of intense research. The majority gate and the
                 inverter gate together make a universal set of Boolean
                 primitives in QCA technology. Reducing the number of
                 required primitives to implement a given Boolean
                 function is an important step in designing QCA logic
                 circuits. Previous research has shown how to use
                 genetic programming to minimise the number of gates
                 implementing a given Boolean function with one output.
                 In this paper, we first show how to minimize the gates
                 for the given Boolean truth tables with an arbitrary
                 number of outputs using genetic programming. Then,
                 another criterion, reduction of the delay of the
                 implementing circuit is considered. Multi-objective
                 genetic programming is applied to simultaneously
                 optimise both objectives. The results demonstrate the
                 proposed approach is promising and worthy of further
                 research.",
  notes =        "Karnaugh maps, clock, multi-objective, Pareto,
                 double-point crossover, multiple output linked trees
                 (DAG), pop 100, 5000 generations, all 3 input Boolean
                 problems, 15percent of all 4 input Boolean problems,
                 roulette wheel selection, minterms.

                 Cites \cite{DBLP:conf/dac/AntonelliCDHKKMN04},
                 \cite{Bonyadi:2007:ICEE}, \cite{Houshmand:2009:ICIS},
                 \cite{Rajaei:2011:SCIA}.",
  affiliation =  "Department of Computer Engineering, Ferdowsi
                 University of Mashhad, Mashhad, Iran",
}

Genetic Programming entries for Razieh Rezaee Mahboobeh Houshmand Monireh Houshmand

Citations