Now showing items 11-14 of 14

  • Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. 

    Petrovski, Andrei; McCall, John (Springer http://dx.doi.org/10.1007/3-540-44719-9, 2001)
    PETROVSKI, A. and MCCALL, J., 2001. Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. In: ZITZLEF, E., DEB, K., THIELE, L., COELLO, C. and CORNE, D. Evolutionary Multi-criterion Optimization : First International Conference, EMO 2001, Zurich, Switzerland, March 2001 : Proceedings. Berlin: Springer. pp. 531-545.
    The main objectives of cancer treatment in general, and of cancer chemotherapy in particular, are to eradicate the tumour and to prolong the patient survival time. Traditionally, treatments are optimised with only ...
  • Multi-objective particle swarm optimisation: methods and applications. 

    Al Moubayed, Noura (Robert Gordon University School of Computing Science and Digital Media, 2014-02)
    Solving real life optimisation problems is a challenging engineering venture. Since the early days of research on optimisation it was realised that many problems do not simply have one optimisation objective. This led to ...
  • Self-learning data processing framework based on computational intelligence: enhancing autonomous control by machine intelligence. 

    Rattadilok, Prapa; Petrovski, Andrei (IEEE http://dx.doi.org/10.1109/EALS.2014.7009508, 2014-12)
    RATTADILOK, P. and PETROVSKI, A., 2014. Self-learning data processing framework based on computational intelligence: enhancing autonomous control by machine intelligence. In: Proceedings of IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 2014. 9-12 December 2014. Piscataway, NJ: IEEE. pp. 87-94.
    A generic framework for evolving and autonomously controlled systems has been developed and evaluated in this paper. A three-phase approach aimed at identification, classification of anomalous data and at prediction of its ...
  • 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 ...