EvoWorkshops 2011, Applications of Evolutionary Computing, January 2011

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Year 2015 : 1 citations

Groba, C., Sartal, A., & Vázquez, X. H. (2015). Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction: An application to fish aggregating devices. Computers & Operations Research, 56, 22-32.

Year 2014 : 4 citations

Groba, C., Sartal, A., & Vázquez, X. H. (2014). Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction: An application to fish aggregating devices. Computers & Operations Research.

Mavrovouniotis, M., & Yang, S. (2014, July). Elitism-based immigrants for ant colony optimization in dynamic environments: Adapting the replacement rate. In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 1752-1759). IEEE.

Tinós, R., Whitley, D., & Howe, A. (2014, July). Use of explicit memory in the dynamic traveling salesman problem. In Proceedings of the 2014 conference on Genetic and evolutionary computation (pp. 999-1006). ACM.

Zhang, Z., Yue, S., Liao, M., & Long, F. (2014). Danger theory based artificial immune system solving dynamic constrained single-objective optimization. Soft Computing, 18(1), 185-206.

Year 2013 : 2 citations

Zhuhong Zhang, Lei Wang, and Min Liao (2013). Adaptive sampling immune algorithm solving joint chance-constrained programming. Journal of Control Theory and Applications 11, no. 2, pp. 237-246, Springer, 2013.

Zhuhong Zhang, Shigang Yue, Min Liao, and Fei Long (2013). Danger theory based artificial immune system solving dynamic constrained single-objective optimization. Soft Computing, pp. 1-22, Springer 2013.

Year 2012 : 1 citations

Patryk Filipiak and Piotr Lipi?ski (2012). “Parallel CHC Algorithm for Solving Dynamic Traveling Salesman Problem Using Many-Core GPU”. Artificial Intelligence: Methodology, Systems, And Applications, Lecture Notes in Computer Science, 2012, Volume 7557/2012, pp. 305-314, Springer, 2012.