| Improving the search performance of SHADE using linear population size reduction R Tanabe, AS Fukunaga 2014 IEEE congress on evolutionary computation (CEC), 1658-1665, 2014 | 1603 | 2014 |
| Success-history based parameter adaptation for differential evolution R Tanabe, A Fukunaga 2013 IEEE congress on evolutionary computation, 71-78, 2013 | 1444 | 2013 |
| An easy-to-use real-world multi-objective optimization problem suite R Tanabe, H Ishibuchi Applied Soft Computing 89, 106078, 2020 | 268 | 2020 |
| Evaluating the performance of SHADE on CEC 2013 benchmark problems R Tanabe, A Fukunaga 2013 IEEE Congress on evolutionary computation, 1952-1959, 2013 | 228 | 2013 |
| A review of evolutionary multimodal multiobjective optimization R Tanabe, H Ishibuchi IEEE Transactions on Evolutionary Computation 24 (1), 193-200, 2019 | 215 | 2019 |
| A decomposition-based evolutionary algorithm for multi-modal multi-objective optimization R Tanabe, H Ishibuchi Parallel Problem Solving from Nature–PPSN XV: 15th International Conference …, 2018 | 119 | 2018 |
| Benchmarking multi-and many-objective evolutionary algorithms under two optimization scenarios R Tanabe, H Ishibuchi, A Oyama IEEE Access 5, 19597-19619, 2017 | 92 | 2017 |
| Reviewing and benchmarking parameter control methods in differential evolution R Tanabe, A Fukunaga IEEE transactions on cybernetics 50 (3), 1170-1184, 2019 | 71 | 2019 |
| A niching indicator-based multi-modal many-objective optimizer R Tanabe, H Ishibuchi Swarm and Evolutionary Computation 49, 134-146, 2019 | 68 | 2019 |
| A framework to handle multimodal multiobjective optimization in decomposition-based evolutionary algorithms R Tanabe, H Ishibuchi IEEE Transactions on Evolutionary Computation 24 (4), 720-734, 2019 | 67 | 2019 |
| A note on constrained multi-objective optimization benchmark problems R Tanabe, A Oyama 2017 IEEE Congress on Evolutionary Computation (CEC), 1127-1134, 2017 | 65 | 2017 |
| Reevaluating exponential crossover in differential evolution R Tanabe, A Fukunaga International Conference on parallel problem solving from nature, 201-210, 2014 | 56 | 2014 |
| Tuning differential evolution for cheap, medium, and expensive computational budgets R Tanabe, A Fukunaga 2015 IEEE Congress on Evolutionary Computation (CEC), 2018-2025, 2015 | 39 | 2015 |
| An analysis of quality indicators using approximated optimal distributions in a 3-D objective space R Tanabe, H Ishibuchi IEEE Transactions on Evolutionary Computation 24 (5), 853-867, 2020 | 35 | 2020 |
| An analysis of control parameters of MOEA/D under two different optimization scenarios R Tanabe, H Ishibuchi Applied Soft Computing 70, 22-40, 2018 | 31 | 2018 |
| Review and analysis of three components of the differential evolution mutation operator in MOEA/D-DE R Tanabe, H Ishibuchi Soft Computing 23 (23), 12843-12857, 2019 | 23 | 2019 |
| Optimization of oil reservoir models using tuned evolutionary algorithms and adaptive differential evolution C Aranha, R Tanabe, R Chassagne, A Fukunaga 2015 IEEE Congress on Evolutionary Computation (CEC), 877-884, 2015 | 22 | 2015 |
| Benchmarking MOEAs for multi-and many-objective optimization using an unbounded external archive R Tanabe, A Oyama Proceedings of the Genetic and Evolutionary Computation Conference, 633-640, 2017 | 18 | 2017 |
| Evaluation of a randomized parameter setting strategy for island-model evolutionary algorithms R Tanabe, A Fukunaga 2013 IEEE Congress on Evolutionary Computation, 1263-1270, 2013 | 18 | 2013 |
| Benchmarking feature-based algorithm selection systems for black-box numerical optimization R Tanabe IEEE Transactions on Evolutionary Computation 26 (6), 1321-1335, 2022 | 17 | 2022 |
Alex FukunagaThe University of TokyoVerified email at idea.c.u-tokyo.ac.jp
