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)
    Shakya, S.K., McCall, J.A.W. & Brown, D.F. (2006). Solving the ising spin glass problem using a bivariate eda based on markov random fields. In proceedings of IEEE Congress on Evolutionary Computation (IEEE CEC 2006), IEEE press, Vancouver, Canada.
     
    Shakya, S.K., McCall, J.A.W. & Brown, D.F. (2005c). Incorporating a metropolis method in a distribution estimation using markov random field algorithm. In proceedings of IEEE Congress on Evolutionary Computation (IEEE CEC 2005), vol. 3, 2576–2583, IEEE press, Edinburgh, UK.
     
    Shakya, S., McCall, J. & Brown, D. (2005b). Using a Markov Network Model in a Univariate EDA: An Emperical Cost-Benefit Analysis. In proceedings of Genetic and Evolutionary Computation COnference (GECCO2005), 727–734, ACM, Washington, D.C., USA.
     
    Shakya, S., McCall, J. & Brown, D. (2005a). Estimating the distribution in an EDA. In B. Ribeiro, R.F. Albrechet, A. Dobnikar, D.W. Pearson & N.C. Steele, eds., In proceedings of the International Conference on Adaptive and Natural computiNG Algorithms (ICANNGA 2005), 202–205, Springer-Verlag, Wien, Coimbra, Portugal.
     
    Shakya, S.K., McCall, J.A.W. & Brown, D.F. (2004b). Updating the probability vector using MRF technique for a Univariate EDA. In E. Onaindia & S. Staab, eds., Proceedings of the Second Starting AI Researchers’ Symposium, volume 109 of Frontiers in artificial Intelligence and Applications, 15–25, IOS press, Valencia, Spain.
     
    Shakya, S.K., McCall, J.A.W. & Brown, D.F. (2004a). Preliminary results on Evolution without Selection. In Proceedings of Postgraduate Research Conference in Electronics, Photonics, Communications and Networks, and Computing Science (PREP 2004), Hertfordshire, UK.
     
    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)
    BROWNLEE, A. E. I., MCCALL, J. A. W. and BROWN, D. F., 2007. Solving the MAXSAT problem using a multivariate EDA based on Markov networks. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2007). 2007. New York: ACM Press. pp. 2423-2428.
     
    BROWNLEE, A. E. I., MCCALL, J. A. W., ZHANG, Q. and BROWN, D., 2008. Approaches to selection and their effect on fitness modeling in an estimation of distribution algorithm. In: Proceedings of the IEEE World Congress on Computational Intelligence (CEC 2008). Piscataway, NJ: IEEE Press. pp. 2621-2628.
     
    BROWNLEE, A. E. I., WU, Y., MCCALL, J. A. W., GODLEY, P. M., CAIRNS, D. E. and COWIE, J., 2008. Optimisation and fitness modelling of bio-control in mushroom farming using a Markov network EDA. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008). New York: ACM Press. pp. 465-466.
     
    BROWNLEE, A. E. I., MCCALL, J. A. W., SHAKYA, S. K. and ZHANG, Q., 2009. Structure learning and optimisation in a Markov-network based estimation of distribution algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2009). Piscataway, NJ: IEEE Press. pp. 447-454.
     
    WU, , Y., MCCALL, J., GODLEY, P., BROWNLEE, A. and CAIRNS, D., 2008. Bio-control in mushroom farming using a Markov network EDA. In: Proceedings of the IEEE World Congress on Computational Intelligence (CEC 2008). Piscataway, NJ: IEEE Press. pp. 2996-3001.
     
    BROWNLEE, A. E. I., PELIKAN, M., MCCALL, J. A. W. and PETROVSKI, A., 2008. An application of a multivariate estimation of distribution algorithm to cancer chemotherapy. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008). New York: ACM Press. pp. 463-464.
     
    SHAKYA, S. K., BROWNLEE, A. E. I., MCCALL, J. A. W., FOURNIER, F. and OWUSU, G., 2009. A fully multivariate DEUM algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2009). Piscataway, NJ: IEEE Press. pp. 479-486.
     
    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 ...