| Finding robust solutions to dynamic optimization problems H Fu, B Sendhoff, K Tang, X Yao European Conference on the Applications of Evolutionary Computation, 616-625, 2013 | 42 | 2013 |
| Robust optimization over time: Problem difficulties and benchmark problems H Fu, B Sendhoff, K Tang, X Yao IEEE Transactions on Evolutionary Computation 19 (5), 731-745, 2014 | 34 | 2014 |
| Parametrized deep q-networks learning: Reinforcement learning with discrete-continuous hybrid action space J Xiong, Q Wang, Z Yang, P Sun, L Han, Y Zheng, H Fu, T Zhang, J Liu, ... arXiv preprint arXiv:1810.06394, 2018 | 27 | 2018 |
| What are dynamic optimization problems? H Fu, PR Lewis, B Sendhoff, K Tang, X Yao 2014 IEEE Congress on Evolutionary Computation (CEC), 1550-1557, 2014 | 26 | 2014 |
| Characterizing environmental changes in robust optimization over time H Fu, B Sendhoff, K Tang, X Yao 2012 IEEE Congress on Evolutionary Computation, 1-8, 2012 | 22 | 2012 |
| Memetic algorithm with heuristic candidate list strategy for capacitated arc routing problem H Fu, Y Mei, K Tang, Y Zhu IEEE Congress on Evolutionary Computation, 1-8, 2010 | 14 | 2010 |
| Find robust solutions over time by two-layer multi-objective optimization method Y Guo, M Chen, H Fu, Y Liu 2014 IEEE Congress on Evolutionary Computation (CEC), 1528-1535, 2014 | 13 | 2014 |
| A Q-learning based evolutionary algorithm for sequential decision making problems H Fu, PR Lewis, X Yao Parallel Problem Solving from Nature (PPSN). VUB AI Lab, 2014 | 3 | 2014 |
| PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE J Xiong, Q Wang, Z Yang, P Sun, Y Zheng, L Han, H Fu, X Lian, ... | 1 | 2018 |
| Finding robust solutions against environmental changes H Fu University of Birmingham, 2014 | | 2014 |
