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2020 – today
- 2024
[j6]Naruhiko Nimura
, Akira Oyama:
Multiobjective Evolutionary Topology Optimization Algorithm Using Quadtree Encoding. IEEE Access 12: 73839-73848 (2024)- 2023
- [c41]Akira Oyama, Shoichi Hasegawa, Hikaru Nakagawa, Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi:
Exophora Resolution of Linguistic Instructions with a Demonstrative based on Real-World Multimodal Information. RO-MAN 2023: 2617-2623 - [p2]Koen van der Blom, Timo M. Deist, Vanessa Volz, Mariapia Marchi, Yusuke Nojima, Boris Naujoks, Akira Oyama, Tea Tusar:
Identifying Properties of Real-World Optimisation Problems Through a Questionnaire. Many-Criteria Optimization and Decision Analysis 2023: 59-80 - 2022
- [j5]Shigetaka Kawai, Wataru Yamazaki, Akira Oyama:
Gegenbauer reconstruction method with edge detection for multi-dimensional uncertainty propagation. J. Comput. Phys. 468: 111505 (2022) - [c40]Naruhiko Nimura, Akira Oyama:
Evolutionary Topology Optimization Using Quadtree Genetic Programming. CEC 2022: 1-8 - [c39]Hodaka Mori, Akira Oyama:
Multi-Objective Evolutionary Algorithm Fully Based on Unbounded Archive for Problems Requiring Very Expensive Solution Evaluations. SSCI 2022: 111-118 - [c38]Quang Nhat Huynh, Hemant K. Singh, Tapabrata Ray, Akira Oyama:
Improved Genetic Programming for Symbolic Regression: Case Studies on Practical Applications. SSCI 2022: 1135-1142 - 2021
- [c37]Motoki Sato, Akira Oyama:
Comparative Study of Crossovers for Decision Space Diversity of Non-Dominated Solutions. SSCI 2021: 1-8 - [c36]Akira Oyama, Hiroto Sato, Kaito Kosuge, Kosuke Uchiyama, Taro Nakamura, Kazunori Umeda:
Detection of Rust from Images in Pipes Using Deep Learning. UR 2021: 476-479 - 2020
- [c35]Koen van der Blom, Timo M. Deist, Tea Tusar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks:
Towards realistic optimization benchmarks: a questionnaire on the properties of real-world problems. GECCO Companion 2020: 293-294 - [c34]Yohanes Bimo Dwianto, Hiroaki Fukumoto, Akira Oyama:
Adaptively preserving solutions in both feasible and infeasible regions on generalized multiple constraint ranking. GECCO 2020: 690-698 - [i2]Koen van der Blom, Timo M. Deist, Tea Tusar, Mariapia Marchi, Yusuke Nojima, Akira Oyama, Vanessa Volz, Boris Naujoks:
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems. CoRR abs/2004.06395 (2020) - [i1]Koen van der Blom, Timo M. Deist, Vanessa Volz, Mariapia Marchi, Yusuke Nojima, Boris Naujoks, Akira Oyama, Tea Tusar:
Identifying Properties of Real-World Optimisation Problems through a Questionnaire. CoRR abs/2011.05547 (2020)
2010 – 2019
- 2019
- [c33]Hiroaki Fukumoto, Akira Oyama:
Coverage Enhancement of MOEA/D-M2M for Problems with Difficult-to-Approximate Pareto Front Boundaries. CEC 2019: 1734-1741 - [c32]Tatsumasa Ishikawa, Hiroaki Fukumoto, Akira Oyama, Hiroyuki Nishida:
Improved binary additive epsilon indicator for obtaining uniformly distributed solutions in multi-objective optimization. GECCO (Companion) 2019: 209-210 - [c31]Yohanes Bimo Dwianto, Hiroaki Fukumoto, Akira Oyama:
On improving the constraint-handling performance with modified multiple constraint ranking (MCR-mod) for engineering design optimization problems solved by evolutionary algorithms. GECCO 2019: 762-770 - [c30]Pramudita Satria Palar, Yohanes Bimo Dwianto, Rommel G. Regis, Akira Oyama, Lavi Rizki Zuhal:
Benchmarking constrained surrogate-based optimization on low speed airfoil design problems. GECCO (Companion) 2019: 1990-1998 - [c29]Aljosa Vodopija, Akira Oyama, Bogdan Filipic:
Ensemble-based constraint handling in multiobjective optimization. GECCO (Companion) 2019: 2072-2075 - [c28]Hiroaki Fukumoto, Akira Oyama:
Impact of Estimation Method of Ideal/Nadir Points on Practically-Constrained Multi-Objective Optimization Problems for Decomposition-Based Multi-Objective Evolutionary Algorithm. SSCI 2019: 2138-2145 - 2018
- [c27]Hiroaki Fukumoto, Akira Oyama:
A Generic Framework for Incorporating Constraint Handling Techniques into Multi-Objective Evolutionary Algorithms. EvoApplications 2018: 634-649 - [c26]Hiroaki Fukumoto, Akira Oyama:
Benchmarking multiobjective evolutionary algorithms and constraint handling techniques on a real-world car structure design optimization benchmark problem. GECCO (Companion) 2018: 177-178 - [c25]Takehisa Kohira, Hiromasa Kemmotsu, Akira Oyama, Tomoaki Tatsukawa:
Proposal of benchmark problem based on real-world car structure design optimization. GECCO (Companion) 2018: 183-184 - [c24]Hiroaki Fukumoto, Akira Oyama:
Study on Improving Efficiency of Multi-Objective Evolutionary Algorithm with Large Population by M2M Decomposition and Elitist Mate Selection Scheme. SSCI 2018: 1180-1187 - 2017
- [j4]Ryoji Tanabe, Hisao Ishibuchi, Akira Oyama:
Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios. IEEE Access 5: 19597-19619 (2017) - [c23]Ryoji Tanabe, Akira Oyama:
A note on constrained multi-objective optimization benchmark problems. CEC 2017: 1127-1134 - [c22]Ryoji Tanabe, Akira Oyama:
The Impact of Population Size, Number of Children, and Number of Reference Points on the Performance of NSGA-III. EMO 2017: 606-621 - [c21]Ryoji Tanabe, Akira Oyama:
Benchmarking MOEAs for multi- and many-objective optimization using an unbounded external archive. GECCO 2017: 633-640 - [c20]Akira Oyama, Takehisa Kohira, Hiromasa Kemmotsu, Tomoaki Tatsukawa, Takeshi Watanabe:
Simultaneous structure design optimization of multiple car models using the K computer. SSCI 2017: 1-4 - 2016
- [c19]Tomoaki Tatsukawa, Takeshi Watanabe, Akira Oyama:
Evolutionary computation for many-objective optimization problems using massive population sizes on the K supercomputer. CEC 2016: 1139-1148 - [c18]Toshiki Kondoh, Tomoaki Tatsukawa, Akira Oyama, Takeshi Watanabe, Kozo Fujii:
Effects of discrete design-variable precision on real-coded Genetic Algorithm. SSCI 2016: 1-8 - 2015
- [c17]Takeshi Watanabe, Tomoaki Tatsukawa, Akira Oyama:
On the fast hypervolume calculation method. CEC 2015: 965-969 - [c16]Yuki Yazawa, Hernán E. Aguirre, Akira Oyama, Kiyoshi Tanaka:
Evolutionary many-objective optimization using dynamic ε-Hoods and Chebyshev function. CEC 2015: 1861-1868 - [c15]Antonio López Jaimes, Akira Oyama, Kozo Fujii:
A ranking method based on two preference criteria: Chebyshev function and ∈-indicator. CEC 2015: 2827-2834 - [c14]Martin Schlueter, Chit Hong Yam, Takeshi Watanabe, Akira Oyama:
Many-objective optimization of interplanetary space mission trajectories. CEC 2015: 3256-3262 - 2014
- [c13]Takeshi Watanabe, Tomoaki Tatsukawa, Antonio López Jaimes, Hikaru Aono, Taku Nonomura, Akira Oyama, Kozo Fujii:
Many-objective evolutionary computation for optimization of separated-flow control using a DBD plasma actuator. IEEE Congress on Evolutionary Computation 2014: 2849-2854 - [c12]Hernán E. Aguirre, Yuki Yazawa, Akira Oyama, Kiyoshi Tanaka:
Extending AεSεH from Many-objective to Multi-objective Optimization. SEAL 2014: 239-250 - [p1]Hernán E. Aguirre, Akira Oyama, Kiyoshi Tanaka:
Distribution Search on Evolutionary Many-Objective Optimization: Selection Mappings and Recombination Rate. Evolution, Complexity and Artificial Life 2014: 241-259 - 2013
- [c11]Antonio López Jaimes, Akira Oyama, Kozo Fujii:
Space trajectory design: Analysis of a real-world many-objective optimization problem. IEEE Congress on Evolutionary Computation 2013: 2809-2816 - [c10]Antonio López Jaimes, Carlos A. Coello Coello, Akira Oyama, Kozo Fujii:
An Alternative Preference Relation to Deal with Many-Objective Optimization Problems. EMO 2013: 291-306 - [c9]Hernán E. Aguirre, Akira Oyama, Kiyoshi Tanaka:
Adaptive ε-Sampling and ε-Hood for Evolutionary Many-Objective Optimization. EMO 2013: 322-336 - [c8]Tomoaki Tatsukawa, Taku Nonomura, Akira Oyama, Kozo Fujii:
A New Multiobjective Genetic Programming for Extraction of Design Information from Non-dominated Solutions. EMO 2013: 528-542 - 2012
- [c7]Akira Oyama, Yasuhiro Kawakatsu, Kazuko Hagiwara:
Data Mining of Pareto-Optimal Solutions of a Solar Observatory Trajectory Design Problem. Infotech@Aerospace 2012 - [c6]Naoya Kowatari, Akira Oyama, Hernán E. Aguirre, Kiyoshi Tanaka:
A Study on Large Population MOEA Using Adaptive ε-Box Dominance and Neighborhood Recombination for Many-Objective Optimization. LION 2012: 86-100 - [c5]Naoya Kowatari, Akira Oyama, Hernán E. Aguirre, Kiyoshi Tanaka:
Analysis on Population Size and Neighborhood Recombination on Many-Objective Optimization. PPSN (2) 2012: 22-31
2000 – 2009
- 2009
- [j3]Akira Oyama, Yoshiyuki Okabe, Koji Shimoyama, Kozo Fujii:
Aerodynamic Multiobjective Design Exploration of a Flapping Airfoil Using a Navier-Stokes Solver. J. Aerosp. Comput. Inf. Commun. 6(3): 256-270 (2009) - [c4]Akira Oyama, Taku Nonomura, Kozo Fujii:
Data mining of non-dominated solutions using proper orthogonal decomposition. GECCO 2009: 1935-1936 - 2008
- [j2]Koji Shimoyama, Akira Oyama, Kozo Fujii:
Development of Multi-Objective Six Sigma Approach for Robust Design Optimization. J. Aerosp. Comput. Inf. Commun. 5(8): 215-233 (2008) - 2005
- [c3]Koji Shimoyama, Akira Oyama, Kozo Fujii:
A new efficient and useful robust optimization approach - design for multi-objective six sigma. Congress on Evolutionary Computation 2005: 950-957 - 2001
- [j1]Akira Oyama, Shigeru Obayashi, Takashi Nakamura:
Real-coded adaptive range genetic algorithm applied to transonic wing optimization. Appl. Soft Comput. 1(3): 179-187 (2001) - 2000
- [c2]Shigeru Obayashi, Akira Oyama, Takashi Nakamura:
Transonic Wing Shape Optimization Based on Evolutionary Algorithms. ISHPC 2000: 172-181 - [c1]Akira Oyama, Shigeru Obayashi, Takashi Nakamura:
Real-Coded Adaptive Range Genetic Algorithm Applied to Transonic Wing Optimization. PPSN 2000: 712-721
Coauthor Index
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last updated on 2024-10-07 21:12 CEST by the dblp team
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