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Ryoji Tanabe
Ryoji Tanabe
Verified email at ynu.ac.jp - Homepage
Title
Cited by
Cited by
Year
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
13902014
Success-history based parameter adaptation for differential evolution
R Tanabe, A Fukunaga
2013 IEEE congress on evolutionary computation, 71-78, 2013
12592013
Evaluating the performance of SHADE on CEC 2013 benchmark problems
R Tanabe, A Fukunaga
2013 IEEE Congress on evolutionary computation, 1952-1959, 2013
2162013
An easy-to-use real-world multi-objective optimization problem suite
R Tanabe, H Ishibuchi
Applied Soft Computing 89, 106078, 2020
2042020
A review of evolutionary multimodal multiobjective optimization
R Tanabe, H Ishibuchi
IEEE Transactions on Evolutionary Computation 24 (1), 193-200, 2019
1722019
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
1022018
Benchmarking multi-and many-objective evolutionary algorithms under two optimization scenarios
R Tanabe, H Ishibuchi, A Oyama
IEEE Access 5, 19597-19619, 2017
872017
Reviewing and benchmarking parameter control methods in differential evolution
R Tanabe, A Fukunaga
IEEE transactions on cybernetics 50 (3), 1170-1184, 2019
602019
A niching indicator-based multi-modal many-objective optimizer
R Tanabe, H Ishibuchi
Swarm and Evolutionary Computation 49, 134-146, 2019
582019
A note on constrained multi-objective optimization benchmark problems
R Tanabe, A Oyama
2017 IEEE Congress on Evolutionary Computation (CEC), 1127-1134, 2017
542017
Reevaluating exponential crossover in differential evolution
R Tanabe, A Fukunaga
International Conference on parallel problem solving from nature, 201-210, 2014
512014
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
492019
Tuning differential evolution for cheap, medium, and expensive computational budgets
R Tanabe, A Fukunaga
2015 IEEE Congress on Evolutionary Computation (CEC), 2018-2025, 2015
372015
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
342020
An analysis of control parameters of MOEA/D under two different optimization scenarios
R Tanabe, H Ishibuchi
Applied Soft Computing 70, 22-40, 2018
302018
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
212015
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
192019
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
182013
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
152017
How far are we from an optimal, adaptive DE?
R Tanabe, A Fukunaga
Parallel Problem Solving from Nature–PPSN XIV: 14th International Conference …, 2016
132016
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