Now showing items 1-1 of 1

  • 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. ...