| A survey on the hypervolume indicator in evolutionary multiobjective optimization K Shang, H Ishibuchi, L He, LM Pang IEEE Transactions on Evolutionary Computation 25 (1), 1-20, 2020 | 218 | 2020 |
| Difficulties in fair performance comparison of multiobjective evolutionary algorithms H Ishibuchi, LM Pang, K Shang Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2022 | 50 | 2022 |
| Application of self-organizing map to failure modes and effects analysis methodology WL Chang, LM Pang, KM Tay Neurocomputing 249, 314-320, 2017 | 46 | 2017 |
| NSGA-II with simple modification works well on a wide variety of many-objective problems LM Pang, H Ishibuchi, K Shang IEEE Access 8, 190240-190250, 2020 | 40 | 2020 |
| A new framework of evolutionary multi-objective algorithms with an unbounded external archive H Ishibuchi, LM Pang, K Shang ECAI 2020, 283-290, 2020 | 40 | 2020 |
| Monotone fuzzy rule relabeling for the zero-order TSK fuzzy inference system LM Pang, KM Tay, CP Lim IEEE Transactions on Fuzzy systems 24 (6), 1455-1463, 2016 | 37 | 2016 |
| Benchmarking large-scale subset selection in evolutionary multi-objective optimization K Shang, T Shu, H Ishibuchi, Y Nan, LM Pang Information Sciences 622, 755-770, 2023 | 16 | 2023 |
| Robust TSK fuzzy system based on semisupervised learning for label noise data T Zhang, Z Deng, H Ishibuchi, LM Pang IEEE Transactions on Fuzzy Systems 29 (8), 2145-2157, 2020 | 16 | 2020 |
| Use of two penalty values in multiobjective evolutionary algorithm based on decomposition LM Pang, H Ishibuchi, K Shang IEEE Transactions on Cybernetics 53 (11), 7174-7186, 2022 | 14 | 2022 |
| Algorithm configurations of MOEA/D with an unbounded external archive LM Pang, H Ishibuchi, K Shang 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2020 | 11 | 2020 |
| Parallel implementation of moea/d with parallel weight vectors for feature selection W Liao, H Ishibuchi, LM Pang, K Shang 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2020 | 10 | 2020 |
| Decomposition-based multi-objective evolutionary algorithm design under two algorithm frameworks LM Pang, H Ishibuchi, K Shang IEEE Access 8, 163197-163208, 2020 | 10 | 2020 |
| Solution subset selection for final decision making in evolutionary multi-objective optimization H Ishibuchi, LM Pang, K Shang arXiv preprint arXiv:2006.08156, 2020 | 10 | 2020 |
| Offline automatic parameter tuning of MOEA/D using genetic algorithm LM Pang, H Ishibuchi, K Shang 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 1889-1897, 2019 | 9 | 2019 |
| Counterintuitive experimental results in evolutionary large-scale multiobjective optimization LM Pang, H Ishibuchi, K Shang IEEE Transactions on Evolutionary Computation 26 (6), 1609-1616, 2022 | 7 | 2022 |
| Hypervolume-Based Cooperative Coevolution with Two Reference Points for Multi-Objective Optimization LM Pang, H Ishibuchi, L He, K Shang, L Chen IEEE Transactions on Evolutionary Computation, 2023 | 6 | 2023 |
| Population size specification for fair comparison of multi-objective evolutionary algorithms H Ishibuchi, LM Pang, K Shang 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2020 | 6 | 2020 |
| Performance evaluation of multi-objective evolutionary algorithms using artificial and real-world problems H Ishibuchi, Y Nan, LM Pang International Conference on Evolutionary Multi-Criterion Optimization, 333-347, 2023 | 5 | 2023 |
| Multi-modal multi-objective test problems with an infinite number of equivalent pareto sets H Ishibuchi, Y Peng, LM Pang 2022 IEEE congress on evolutionary computation (CEC), 1-8, 2022 | 5 | 2022 |
| Using a genetic algorithm-based hyper-heuristic to tune MOEA/D for a set of various test problems LM Pang, H Ishibuchi, K Shang 2021 IEEE Congress on Evolutionary Computation (CEC), 1486-1494, 2021 | 5 | 2021 |
Hisao IshibuchiSouthern University of Science and Technology在 sustech.edu.cn 的电子邮件经过验证
