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Ke Shang
Publications
- 2024
- [j46]Ke Shang, Tianye Shu, Hisao Ishibuchi:
Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation. IEEE Trans. Evol. Comput. 28(1): 105-116 (2024) - 2023
- [j43]Ke Shang, Tianye Shu, Hisao Ishibuchi, Yang Nan, Lie Meng Pang:
Benchmarking large-scale subset selection in evolutionary multi-objective optimization. Inf. Sci. 622: 755-770 (2023) - [j41]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Use of Two Penalty Values in Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Trans. Cybern. 53(11): 7174-7186 (2023) - [j40]Tianye Shu, Ke Shang, Hisao Ishibuchi, Yang Nan:
Effects of Archive Size on Computation Time and Solution Quality for Multiobjective Optimization. IEEE Trans. Evol. Comput. 27(4): 1145-1153 (2023) - [j39]Ke Shang, Weiyu Chen, Weiduo Liao, Hisao Ishibuchi:
HV-Net: Hypervolume Approximation Based on DeepSets. IEEE Trans. Evol. Comput. 27(4): 1154-1160 (2023) - [j38]Linjun He, Ke Shang, Yang Nan, Hisao Ishibuchi, Dipti Srinivasan:
Relation Between Objective Space Normalization and Weight Vector Scaling in Decomposition-Based Multiobjective Evolutionary Algorithms. IEEE Trans. Evol. Comput. 27(5): 1177-1191 (2023) - [j37]Yang Nan, Ke Shang, Hisao Ishibuchi, Linjun He:
An Improved Local Search Method for Large-Scale Hypervolume Subset Selection. IEEE Trans. Evol. Comput. 27(6): 1690-1704 (2023) - [c52]Yang Nan, Hisao Ishibuchi, Tianye Shu, Ke Shang:
Two-Stage Greedy Approximated Hypervolume Subset Selection for Large-Scale Problems. EMO 2023: 391-404 - [c51]Guangyan An, Ziyu Wu, Zhilong Shen, Ke Shang, Hisao Ishibuchi:
Evolutionary Multi-Objective Deep Reinforcement Learning for Autonomous UAV Navigation in Large-Scale Complex Environments. GECCO 2023: 633-641 - [c50]Hisao Ishibuchi, Lie Meng Pang, Ke Shang:
Effects of Dominance Modification on Hypervolume-based and IGD-based Performance Evaluation Results of NSGA-II. GECCO 2023: 679-687 - [c49]Tianye Shu, Yang Nan, Ke Shang, Hisao Ishibuchi:
Two-Phase Procedure for Efficiently Removing Dominated Solutions From Large Solution Sets. GECCO 2023: 740-748 - [c48]Han Zhu, Ke Shang, Hisao Ishibuchi:
STHV-Net: Hypervolume Approximation based on Set Transformer. GECCO 2023: 804-812 - [c47]Ke Shang, Tianye Shu, Guotong Wu, Yang Nan, Lie Meng Pang, Hisao Ishibuchi:
Empirical Hypervolume Optimal µ-Distributions on Complex Pareto Fronts. SSCI 2023: 433-440 - [c46]Tianye Shu, Yang Nan, Ke Shang, Hisao Ishibuchi:
Analysis of Partition Methods for Dominated Solution Removal from Large Solution Sets. SSCI 2023: 441-448 - [c45]Guotong Wu, Tianye Shu, Ke Shang, Hisao Ishibuchi:
Normalization in R2-Based Hypervolume and Hypervolume Contribution Approximation. SSCI 2023: 449-456 - [c44]Guotong Wu, Tianye Shu, Yang Nan, Ke Shang, Hisao Ishibuchi:
Ensemble R2-based Hypervolume Contribution Approximation. SSCI 2023: 1503-1510 - 2022
- [j36]Hisao Ishibuchi, Lie Meng Pang, Ke Shang:
Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary Algorithms [Research Frontier]. IEEE Comput. Intell. Mag. 17(1): 86-101 (2022) - [j31]Ke Shang, Hisao Ishibuchi, Weiyu Chen, Yang Nan, Weiduo Liao:
Hypervolume-Optimal μ-Distributions on Line/Plane-Based Pareto Fronts in Three Dimensions. IEEE Trans. Evol. Comput. 26(2): 349-363 (2022) - [j30]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Fast Greedy Subset Selection From Large Candidate Solution Sets in Evolutionary Multiobjective Optimization. IEEE Trans. Evol. Comput. 26(4): 750-764 (2022) - [j29]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Counterintuitive Experimental Results in Evolutionary Large-Scale Multiobjective Optimization. IEEE Trans. Evol. Comput. 26(6): 1609-1616 (2022) - [c43]Hisao Ishibuchi, Lie Meng Pang, Ke Shang:
Difficulties in fair performance comparison of multiobjective evolutionary algorithms. GECCO Companion 2022: 937-957 - [c42]Tianye Shu, Ke Shang, Yang Nan, Hisao Ishibuchi:
Direction Vector Selection for R2-Based Hypervolume Contribution Approximation. PPSN (2) 2022: 110-123 - [c41]Ke Shang, Weiduo Liao, Hisao Ishibuchi:
HVC-Net: Deep Learning Based Hypervolume Contribution Approximation. PPSN (1) 2022: 414-426 - [i13]Ke Shang, Tianye Shu, Hisao Ishibuchi, Yang Nan, Lie Meng Pang:
Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization. CoRR abs/2201.06700 (2022) - [i12]Ke Shang, Tianye Shu, Hisao Ishibuchi:
Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation. CoRR abs/2201.06707 (2022) - [i11]Ke Shang, Weiyu Chen, Weiduo Liao, Hisao Ishibuchi:
HV-Net: Hypervolume Approximation based on DeepSets. CoRR abs/2203.02185 (2022) - [i10]Tianye Shu, Ke Shang, Hisao Ishibuchi, Yang Nan:
Effects of Archive Size on Computation Time and Solution Quality for Multi-Objective Optimization. CoRR abs/2209.03100 (2022) - 2021
- [j24]Ke Shang, Hisao Ishibuchi, Linjun He, Lie Meng Pang:
A Survey on the Hypervolume Indicator in Evolutionary Multiobjective Optimization. IEEE Trans. Evol. Comput. 25(1): 1-20 (2021) - [c40]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Using a Genetic Algorithm-based Hyper-heuristic to Tune MOEA/D for a Set of Various Test Problems. CEC 2021: 1486-1494 - [c39]Longcan Chen, Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Periodical Generation Update using an Unbounded External Archive for Multi-Objective Optimization. CEC 2021: 1912-1920 - [c38]Yang Nan, Ke Shang, Hisao Ishibuchi, Linjun He:
A Two-stage Hypervolume Contribution Approximation Method Based on R2 Indicator. CEC 2021: 2468-2475 - [c37]Ke Shang, Hisao Ishibuchi, Longcan Chen, Weiyu Chen, Lie Meng Pang:
Improving the Efficiency of R2HCA-EMOA. EMO 2021: 115-125 - [c36]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Using a Genetic Algorithm-Based Hyper-Heuristic to Tune MOEA/D for a Set of Benchmark Test Problems. EMO 2021: 164-177 - [c35]Ke Shang, Hisao Ishibuchi, Yang Nan:
Distance-based subset selection revisited. GECCO 2021: 439-447 - [c34]Ke Shang, Hisao Ishibuchi, Weiyu Chen:
Greedy approximated hypervolume subset selection for many-objective optimization. GECCO 2021: 448-456 - [c33]Jinyuan Zhang, Hisao Ishibuchi, Ke Shang, Linjun He, Lie Meng Pang, Yiming Peng:
Environmental selection using a fuzzy classifier for multiobjective evolutionary algorithms. GECCO 2021: 485-492 - [c32]Ke Shang, Hisao Ishibuchi, Lie Meng Pang, Yang Nan:
Reference Point Specification for Greedy Hypervolume Subset Selection. SMC 2021: 168-175 - [c31]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization. SMC 2021: 468-475 - [c30]Lie Meng Pang, Ke Shang, Longcan Chen, Hisao Ishibuchi, Weiyu Chen:
Proposal of a New Test Problem for Large-Scale Multi- and Many-Objective Optimization. SMC 2021: 484-491 - [c29]Yang Nan, Ke Shang, Hisao Ishibuchi, Linjun He:
Improving Local Search Hypervolume Subset Selection in Evolutionary Multi-objective Optimization. SMC 2021: 751-757 - [c28]Longcan Chen, Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Periodical Weight Vector Update Using an Unbounded External Archive for Decomposition-Based Evolutionary Multi-Objective Optimization. SSCI 2021: 1-8 - [i9]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization. CoRR abs/2102.00941 (2021) - [i8]Ke Shang, Hisao Ishibuchi, Weiyu Chen, Yang Nan, Weiduo Liao:
Hypervolume-Optimal μ-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions. CoRR abs/2104.09736 (2021) - [i7]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization. CoRR abs/2108.08453 (2021) - 2020
- [j21]Yang Nan, Ke Shang, Hisao Ishibuchi, Linjun He:
Reverse Strategy for Non-Dominated Archiving. IEEE Access 8: 119458-119469 (2020) - [j20]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Decomposition-Based Multi-Objective Evolutionary Algorithm Design Under Two Algorithm Frameworks. IEEE Access 8: 163197-163208 (2020) - [j19]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems. IEEE Access 8: 190240-190250 (2020) - [j18]Linjun He, Ke Shang, Hisao Ishibuchi:
Simultaneous use of two normalization methods in decomposition-based multi-objective evolutionary algorithms. Appl. Soft Comput. 92: 106316 (2020) - [j11]Ke Shang, Hisao Ishibuchi, Xizi Ni:
R2-Based Hypervolume Contribution Approximation. IEEE Trans. Evol. Comput. 24(1): 185-192 (2020) - [j10]Ke Shang, Hisao Ishibuchi, Xizi Ni:
Erratum to "R2-Based Hypervolume Contribution Approximation". IEEE Trans. Evol. Comput. 24(4): 807 (2020) - [j9]Ke Shang, Hisao Ishibuchi:
A New Hypervolume-Based Evolutionary Algorithm for Many-Objective Optimization. IEEE Trans. Evol. Comput. 24(5): 839-852 (2020) - [c27]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Modified Distance-based Subset Selection for Evolutionary Multi-objective Optimization Algorithms. CEC 2020: 1-8 - [c26]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Lazy Greedy Hypervolume Subset Selection from Large Candidate Solution Sets. CEC 2020: 1-8 - [c25]Hisao Ishibuchi, Lie Meng Pang, Ke Shang:
A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive. ECAI 2020: 283-290 - [c24]Yang Nan, Ke Shang, Hisao Ishibuchi:
What is a good direction vector set for the R2-based hypervolume contribution approximation. GECCO 2020: 524-532 - [c23]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Proposal of a Realistic Many-Objective Test Suite. PPSN (1) 2020: 201-214 - [c22]Ke Shang, Hisao Ishibuchi, Weiyu Chen, Lukás Adam:
Hypervolume Optimal μ-Distributions on Line-Based Pareto Fronts in Three Dimensions. PPSN (2) 2020: 257-270 - [c21]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Algorithm Configurations of MOEA/D with an Unbounded External Archive. SMC 2020: 1087-1094 - [c20]Hisao Ishibuchi, Lie Meng Pang, Ke Shang:
Population Size Specification for Fair Comparison of Multi-objective Evolutionary Algorithms. SMC 2020: 1095-1102 - [c19]Hisao Ishibuchi, Lie Meng Pang, Ke Shang:
Numerical Analysis on Optimal Distributions of Solutions for Hypervolume Maximization. SMC 2020: 1103-1110 - [c18]Weiduo Liao, Hisao Ishibuchi, Lie Meng Pang, Ke Shang:
Parallel Implementation of MOEA/D with Parallel Weight Vectors for Feature Selection. SMC 2020: 1524-1531 - [c17]Ke Shang, Hisao Ishibuchi, Yang Nan, Weiyu Chen:
Transformation-based Hypervolume Indicator: A Framework for Designing Hypervolume Variants. SSCI 2020: 157-164 - [c16]Longcan Chen, Ke Shang, Hisao Ishibuchi:
Performance Comparison of Multi-Objective Evolutionary Algorithms on Simple and Difficult Many-Objective Test Problems. SSCI 2020: 2461-2468 - [i6]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms. CoRR abs/2003.09917 (2020) - [i5]Hisao Ishibuchi, Lie Meng Pang, Ke Shang:
Solution Subset Selection for Final Decision Making in Evolutionary Multi-Objective Optimization. CoRR abs/2006.08156 (2020) - [i4]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Lazy Greedy Hypervolume Subset Selection from Large Candidate Solution Sets. CoRR abs/2007.02050 (2020) - [i3]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Algorithm Configurations of MOEA/D with an Unbounded External Archive. CoRR abs/2007.13352 (2020) - [i2]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks. CoRR abs/2008.07094 (2020) - [i1]Hisao Ishibuchi, Lie Meng Pang, Ke Shang:
Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets. CoRR abs/2012.07319 (2020) - 2019
- [c15]Hisao Ishibuchi, Yiming Peng, Ke Shang:
A Scalable Multimodal Multiobjective Test Problem. CEC 2019: 310-317 - [c14]Kanzhen Wan, Cheng He, Auraham Camacho, Ke Shang, Ran Cheng, Hisao Ishibuchi:
A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. CEC 2019: 2018-2025 - [c13]Hisao Ishibuchi, Linjun He, Ke Shang:
Regular Pareto Front Shape is not Realistic. CEC 2019: 2034-2041 - [c12]Weiyu Chen, Hisao Ishibuchi, Ke Shang:
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multi-objective Optimization Algorithms. SSCI 2019: 1826-1833 - [c11]Linjun He, Yang Nan, Ke Shang, Hisao Ishibuchi:
A Study of the Naïve Objective Space Normalization Method in MOEA/D. SSCI 2019: 1834-1840 - [c10]Weiduo Liao, Ke Shang, Lie Meng Pang, Hisao Ishibuchi:
Weak Convergence Detection-based Dynamic Reference Point Specification in SMS-EMOA. SSCI 2019: 1841-1848 - [c9]Yiming Peng, Hisao Ishibuchi, Ke Shang:
Multi-modal Multi-objective Optimization: Problem Analysis and Case Studies. SSCI 2019: 1865-1872 - [c8]Lie Meng Pang, Hisao Ishibuchi, Ke Shang:
Offline Automatic Parameter Tuning of MOEA/D Using Genetic Algorithm. SSCI 2019: 1889-1897 - 2018
- [c7]Ke Shang, Hisao Ishibuchi, Min-Ling Zhang, Yiping Liu:
A new R2 indicator for better hypervolume approximation. GECCO 2018: 745-752 - [c6]Xizi Ni, Hisao Ishibuchi, Kanzhen Wan, Ke Shang, Chukun Zhuang:
Weight vector grid with new archive update mechanism for multi-objective optimization. GECCO (Companion) 2018: 1906-1909 - [c5]Yiping Liu, Hisao Ishibuchi, Yusuke Nojima, Naoki Masuyama, Ke Shang:
A Double-Niched Evolutionary Algorithm and Its Behavior on Polygon-Based Problems. PPSN (1) 2018: 262-273 - [c4]Yiping Liu, Hisao Ishibuchi, Yusuke Nojima, Naoki Masuyama, Ke Shang:
Improving 1by1EA to Handle Various Shapes of Pareto Fronts. PPSN (1) 2018: 311-322
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