Evolutionary Computation for Dynamic Optimisation in Network Environments


 

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  • M. Mavrovouniotis, S. Yang. Elitism-based immigrants for ant colony optimization in dynamic environments: adapting the replacement rate. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 1752-1759, IEEE Press, 2014.
  • M. Mavrovouniotis, S. Yang. Interactive and non-interactive hybrid immigrants schemes for ant algorithms in dynamic environments. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 1542-1549, IEEE Press, 2014.
  • M. Mavrovouniotis, S. Yang. Training neural networks with ant colony optimization algorithms for pattern classification. Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer-Verlag, 2014.
  • M. Mavrovouniotis, S. Yang. Evolving neural networks using ant colony optimization with pheromone trail limits. Proceedings of the 2013 UK Workshop on Computational Intelligence, pp. 16-23, IEEE Press, 2013.
  • M. Mavrovouniotis, S. Yang. Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors. Applied Soft Computing, vol.13, no. 10, pp. 4023-4037, Elsevier, 2013.
  • M. Mavrovouniotis, S. Yang. Genetic algorithms with adaptive immigrants for dynamic environments. Proceedings of the 2013 IEEE Congress on Evolutionary Computation, pp. 2130-2137, IEEE Press, 2013.
  • M. Mavrovouniotis, S. Yang. Dynamic vehicle routing: A memetic ant colony optimization approach. A.S. Uyar, E. Ozcan and N. Urquhart (eds.) Automated Scheduling and Planning, Studies in Computational Intelligence, vol. 505, Chapter 9, pp. 283-301, Springer-Verlag, 2013.
  • M. Mavrovouniotis, S. Yang. Adapting the pheromone evaporation rate in dynamic routing problems. EvoApplications 2013: Applications of Evolutionary Computation, LNCS, vol. 7835, pp. 606-615. Springer-Verlag, 2013.