Self-Assembly Quantum Dots Growth Prediction by Quantum-Inspired Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  title =        "Self-Assembly Quantum Dots Growth Prediction by
                 Quantum-Inspired Linear Genetic Programming",
  author =       "Douglas Dias and Anderson Singulani and 
                 Marco Aurelio Pacheco and Patricia Souza and Mauricio Pires and 
                 Omar Vilela Neto",
  pages =        "2060--2067",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
  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, artificial
                 neural network, growth parameters database, machine
                 code programs, quantum dot mean height behaviour,
                 quantum inspired linear genetic programming, self
                 assembly quantum dots growth prediction, quantum
                 computing, quantum dots",
  DOI =          "doi:10.1109/CEC.2011.5949871",
  abstract =     "In this work we present the application of quantum
                 inspired linear genetic programming (QILGP) to the
                 growth of self-assembled quantum dots. Quantum inspired
                 linear genetic programming is a novel model to evolve
                 machine code programs exploiting quantum mechanics
                 principles. Quantum dots are nanostructures that have
                 been widely applied to optoelectronics devices. The
                 method proposed here relies on an existing database of
                 growth parameters with a resulting quantum dot
                 characteristic to be able to later obtain the growth
                 parameters needed to reach a specific value for such a
                 quantum dot characteristic. The computational
                 techniques were used to associate the growth input
                 parameters with the mean height of the deposited
                 quantum dots. Trends of the quantum dot mean height
                 behaviour as a function of growth parameters were
                 correctly predicted, improving on the results obtained
                 by artificial neural network and classical genetic
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",

Genetic Programming entries for Douglas Mota Dias Anderson Pires Singulani Marco Aurelio Cavalcanti Pacheco Patricia L Souza Mauricio P Pires Omar Paranaiba Vilela Neto