Now showing items 1-4 of 4

  • DEUM: A Framework for an Estimation of Distribution Algorithm based on Markov Random Fields 

    Shakya, Siddhartha (The Robert Gordon University School of Computing, Faculty of Design and Technology, The Robert Gordon University, Aberdeen, UK, 2006-04)
    Estimation of Distribution Algorithms (EDAs) belong to the class of population based optimisation algorithms. They are motivated by the idea of discovering and exploiting the interaction between variables in the solution. ...
  • Incorporating a metropolis method in a distribution estimation using Markov random field algorithm. 

    Shakya, Siddhartha; McCall, John; Brown, Deryck (IEEE http://dx.doi.org/10.1109/CEC.2005.1555017, 2005-09)
    SHAKYA, S., MCCALL, J. and BROWN, D., 2005. Incorporating a metropolis method in a distribution estimation using Markov random field algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), Volume 3. 2-5 September 2005. New York: IEEE. pp. 2576-2583.
    Markov Random Field (MRF) modelling techniques have been recently proposed as a novel approach to probabilistic modelling for Estimation of Distribution Algorithms (EDAs)[34, 4]. An EDA using this technique, presented ...
  • Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm. 

    Brownlee, Alexander Edward Ian (The Robert Gordon University School of Computing, 2009-05)
    A well-known paradigm for optimisation is the evolutionary algorithm (EA). An EA maintains a population of possible solutions to a problem which converges on a global optimum using biologically-inspired selection and ...
  • Solving the ising spin glass problem using a bivariate RDA based on Markov random fields. 

    Shakya, Siddhartha; McCall, John; Brown, Deryck (IEEE http://dx.doi.org/10.1109/CEC.2006.1688408, 2006-07)
    SHAKYA, S., MCCALL, J. and BROWN, D., 2006. Solving the ising spin glass problem using a bivariate RDA based on Markov random fields. In: YEN, G., LUCAS, S., FOGEL, G., KENDALL, G., SALOMON, R., ZHANG, B.-T., COELLO, C. and RUNARSSON, T., eds. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2006). 16-21 July 2006. New York: IEEE. pp. 908-915.
    Markov Random Field (MRF) modelling techniques have been recently proposed as a novel approach to probabilistic modelling for Estimation of Distribution Algorithms (EDAs). An EDA using this technique was called Distribution ...