By Avi Ostfeld
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Extra resources for Ant Colony Optimization - Methods and Applications
504–510. pdf Sim˜oes, A. & Costa, E. (2001). Using biological inspiration to deal with dynamic environments, Proceedings of the Seventh International Conference on Soft Computing (MENDEL01), Brno, Czech Republic. Socha, K. (2004). ACO for Continuous and Mixed-Variable Optimization, in M. Dorigo & al. (eds), ANTS 2004, LNCS 3172, pp. 25–36. , Dr´eo, J. & Siarry, P. (2007). Fitting of an ant colony approach to dynamic optimization through a new set of test functions, International Journal of Computational Intelligence Research 3(3): 205–218.
R. (1997). On the Use of Niching for Dynamic Landscapes, International Conference on Evolutionary Computation, IEEE. Cobb, H. G. & Grefenstette, J. J. (1993). Genetic Algorithms for Tracking Changing Environments, International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 523–530. ps Dorigo, M. & Gambardella, L. M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Transactions on Evolutionary Computation 1(1): 53–66. Dorigo, M. & Gambardella, L.
The Shifting Balance Genetic Algorithm: Improving the GA in a Dynamic Environment, Genetic and Evolutionary Computation Conference (GECCO), Vol. 1, Morgan Kaufmann, San Francisco, pp. 504–510. pdf Sim˜oes, A. & Costa, E. (2001). Using biological inspiration to deal with dynamic environments, Proceedings of the Seventh International Conference on Soft Computing (MENDEL01), Brno, Czech Republic. Socha, K. (2004). ACO for Continuous and Mixed-Variable Optimization, in M. Dorigo & al. (eds), ANTS 2004, LNCS 3172, pp.