Now showing items 1-3 of 3

  • Generating easy and hard problems using the Proximate Optimality Principle. 

    McCall, John; Christie, Lee A.; Brownlee, Alexander Edward Ian (ACM http://doi.acm.org/10.1145/2739482.2764890, 2015)
    MCCALL, J. A. W., CHRISTIE, L. A. and BROWNLEE, A. E. I, 2015. Generating easy and hard problems using the Proximate Optimality Principle. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO Companion '15). New York: ACM. pp. 767-768.
    We present an approach to generating problems of variable difficulty based on the well-known Proximate Optimality Principle (POP), often paraphrased as "similar solutions have similar fitness". We explore definitions of ...
  • 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 ...
  • 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 ...