Now showing items 22-28 of 28

  • Partial structure learning by subset Walsh transform. 

    Christie, Lee A.; Lonie, David P.; McCall, John (IEEE http://dx.doi.org/10.1109/UKCI.2013.6651297, 2013)
    CHRISTIE, L. A., LONIE, D. P., and McCALL, J. A. W., 2013. Partial structure learning by subset Walsh transform. In: Proceedings of the 2013 UK Workshop on Computational Intelligence (UKCI), 2013. IEEE Press, pp. 128-135.
    Estimation of distribution algorithms (EDAs) use structure learning to build a statistical model of good solutions discovered so far, in an effort to discover better solutions. The non-zero coefficients of the Walsh ...
  • Problem dependent metaheuristic performance in Bayesian network structure learning. 

    Wu, Yanghui (Robert Gordon University School of Computing Science and Digital Media, 2012-09)
    Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. ...
  • A sequence-length sensitive approach to learning biological grammars using inductive logic programming. 

    Mamer, Thierry (Robert Gordon University School of Computing, 2011-01)
    This thesis aims to investigate if the ideas behind compression principles, such as the Minimum Description Length, can help us to improve the process of learning biological grammars from protein sequences using Inductive ...
  • 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 ...
  • Statistical optimisation and tuning of GA factors. 

    Petrovski, Andrei; Brownlee, Alexander Edward Ian; McCall, John (IEEE http://dx.doi.org/10.1109/CEC.2005.1554759, 2005-09)
    PETROVSKI, A., BROWNLEE, A. and MCCALL, J., 2005. Statistical optimisation and tuning of GA factors. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), Volume 1. 2-5 September 2005. New York: IEEE. pp. 758-764.
    This paper presents a practical methodology of improving the efficiency of Genetic Algorithms through tuning the factors significantly affecting GA performance. This methodology is based on the methods of statistical ...
  • Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems. 

    Browlnee, Alexander Edward Ian; McCall, John; Christie, Lee A. (IEEE http://dx.doi.org/10.1109/CEC.2015.7257139, 2015-05)
    BROWNLEE, A. E. I., MCCALL, J. A. W. and CHRISTIE, L. A., 2015. Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems. In: 2015 IEEE Congress on Evolutionary Computation (CEC): Proceedings. 25-28 May 2015. Piscataway, NJ: IEEE. Pp. 2066-2073.
    Metaheuristics assume some kind of coherence between decision and objective spaces. Estimation of Distribution algorithms approach this by constructing an explicit probabilistic model of high fitness solutions, the ...
  • Temporal patterns in artificial reaction networks. 

    Gerrard, Claire E.; McCall, John; Coghill, George M.; Macleod, Christopher (Springer Verlag. http://www.springerlink.com/content/?k=(lncs+7552)+AND+(claire+gerrard), 2012-09)
    GERRARD, C., MCCALL, J., COGHILL, G. M. and MACLEOD, C., 2012. Temporal patterns in artificial reaction networks. In: A. E. P. VILLA et al (eds.) Artificial Neural Networks and Machine Learning – ICANN 2012: 22nd Annual Conference on Artificial Neural Networks. 11-14 September 2012. Berlin: Springer. Pp. 1-8.
    The Artificial Reaction Network (ARN) is a bio-inspired connection-ist paradigm based on the emerging field of Cellular Intelligence. It has proper-ties in common with both AI and Systems Biology techniques including ...