Controlling dominance area of solutions and its impact on the performance of MOEAs H Sato, HE Aguirre, K Tanaka International conference on evolutionary multi-criterion optimization, 5-20, 2007 | 384 | 2007 |
Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization H Sato Proceedings of the 2014 Annual Conference on Genetic and Evolutionary …, 2014 | 117 | 2014 |
Self-controlling dominance area of solutions in evolutionary many-objective optimization H Sato, HE Aguirre, K Tanaka Asia-Pacific Conference on Simulated Evolution and Learning, 455-465, 2010 | 83 | 2010 |
Evolutionary many-objective optimization H Ishibuchi, H Sato Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 71 | 2019 |
Analysis of inverted PBI and comparison with other scalarizing functions in decomposition based MOEAs H Sato Journal of Heuristics 21 (6), 819-849, 2015 | 66 | 2015 |
Local dominance using polar coordinates to enhance multiobjective evolutionary algorithms H Sato, HE Aguirre, K Tanaka Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No …, 2004 | 58 | 2004 |
Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems H Sato, H Aguirre, K Tanaka Annals of Mathematics and Artificial Intelligence 68 (4), 197-224, 2013 | 46 | 2013 |
Pareto partial dominance MOEA and hybrid archiving strategy included CDAS in many-objective optimization H Sato, HE Aguirre, K Tanaka IEEE Congress on Evolutionary Computation, 1-8, 2010 | 39 | 2010 |
Pareto partial dominance MOEA and hybrid archiving strategy included CDAS in many-objective optimization H Sato, HE Aguirre, K Tanaka IEEE Congress on Evolutionary Computation, 1-8, 2010 | 39 | 2010 |
Local dominance and local recombination in MOEAs on 0/1 multiobjective knapsack problems H Sato, HE Aguirre, K Tanaka European Journal of Operational Research 181 (3), 1708-1723, 2007 | 39 | 2007 |
Two-stage non-dominated sorting and directed mating for solving problems with multi-objectives and constraints M Miyakawa, K Takadama, H Sato Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013 | 36 | 2013 |
Genetic diversity and effective crossover in evolutionary many-objective optimization H Sato, HE Aguirre, K Tanaka International Conference on Learning and Intelligent Optimization, 91-105, 2011 | 27 | 2011 |
Improved S-CDAs using crossover controlling the number of crossed genes for many-objective optimization H Sato, H Aguirre, K Tanaka Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011 | 26 | 2011 |
Weight Vector Arrangement Using Virtual Objective Vectors in Decomposition-based MOEA T Takagi, K Takadama, H Sato 2021 IEEE Congress on Evolutionary Computation (CEC), 1462-1469, 2021 | 21 | 2021 |
XCSR based on compressed input by deep neural network for high dimensional data K Matsumoto, R Takano, T Tatsumi, H Sato, T Kovacs, K Takadama Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2018 | 15 | 2018 |
Knowledge extraction from XCSR based on dimensionality reduction and deep generative models M Tadokoro, S Hasegawa, T Tatsumi, H Sato, K Takadama 2019 IEEE Congress on Evolutionary Computation (CEC), 1883-1890, 2019 | 14 | 2019 |
On the locality of dominance and recombination in multiobjective evolutionary algorithms H Sato, HE Aguirre, K Tanaka 2005 IEEE Congress on Evolutionary Computation 1, 451-458, 2005 | 14 | 2005 |
Incremental lattice design of weight vector set T Takagi, K Takadama, H Sato Proceedings of the 2020 Genetic and Evolutionary Computation Conference …, 2020 | 13 | 2020 |
A distribution control of weight vector set for multi-objective evolutionary algorithms T Takagi, K Takadama, H Sato International Conference on Bio-inspired Information and Communication, 70-80, 2019 | 13 | 2019 |
Controlling selection area of useful infeasible solutions in directed mating for evolutionary constrained multiobjective optimization M Miyakawa, K Takadama, H Sato International Conference on Learning and Intelligent Optimization, 137-152, 2014 | 13 | 2014 |