Now showing items 1-3 of 3

  • BPGA-EDA for the multi-mode resource constrained project scheduling problem. 

    Ayodele, Mayowa; McCall, John; Regnier-Coudert, Olivier (IEEE https://doi.org/10.1109/CEC.2016.7744222, 2016-11-21)
    AYODELE, M., MCCALL, J. and REGNIER-COUDERT, O. 2016. BPGA-EDA for the multi-mode resource constrained project scheduling problem. In the Proceedings of the IEEE congress on evolutionary computation (CEC), 24-29 July 2016, Vancouver, Canada. Piscataway, NJ: IEEE [online], pages 3417-3424. Available from: https://doi.org/10.1109/CEC.2016.7744222
    The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) has been of research interest for over two decades. The problem is composed of two interacting sub problems: mode assignment and activity scheduling. ...
  • The evolution of modular artificial neural networks 

    Muthuraman, Sethuraman (The Robert Gordon University School of Engineering, 2005)
    This thesis describes a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary ...
  • Evolutionary algorithms for real-time artificial neural network training. 

    Jagadeesan, Ananda Prasanna; Maxwell, Grant M.; Macleod, Christopher (Springer https://dx.doi.org/10.1007/11550907_12, 2005-09-01)
    JAGADEESAN, A., MAXWELL, G. and MACLEOD, C. 2005. Evolutionary algorithms for real-time artificial neural network training. Lecture notes in computer science [online], 3697, Proceedings of the 15th international conference on artifical neural networks (ICANN 2005): formal models and their applications, 11-15 September 2005, Warsaw, Poland, part 2, pages 73-78. Available from: https://dx.doi.org/10.1007/11550907_12
    This paper reports on experiments investigating the use of Evolutionary Algorithms to train Artificial Neural Networks in real time. A simulated legged mobile robot was used as a test bed in the experiments. Since the ...