Naoki Hamada

According to our database, Naoki Hamada authored at least 22 papers between 2008 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization.

Akiyoshi Sannai, Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Naoki Hamada

Trans. Mach. Learn. Res., 2025

Stochastic Gradient Descent for Bézier Simplex Representation of Pareto Set in Multi-Objective Optimization.

Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Akiyoshi Sannai, Naoki Hamada

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Visualization of Multiobjective Multimodal Benchmarking Based on Basin Connectivity.

Likun Liu, Ryosuke Ota, Takahiro Yamamoto, Naoki Hamada, Daisuke Sakurai

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

2023

GenéLive! Generating Rhythm Actions in Love Live!

Atsushi Takada, Daichi Yamazaki, Yudai Yoshida, Nyamkhuu Ganbat, Takayuki Shimotomai, Naoki Hamada, Likun Liu, Taiga Yamamoto, Daisuke Sakurai

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Free Disposal Hull Condition to Verify When Efficiency Coincides with Weak Efficiency.

Naoki Hamada, Shunsuke Ichiki

J. Optim. Theory Appl., 2022

A two-phase framework with a bézier simplex-based interpolation method for computationally expensive multi-objective optimization.

Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi, Hiroshi Umeki, Shinichi Shirakawa, Naoki Hamada

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

2021

Explicitly Multi-Modal Benchmarks for Multi-Objective Optimization.

Reiya Hagiwara, Takahiro Yamamoto, Naoki Hamada, Daisuke Sakurai

CoRR, 2021

All unconstrained strongly convex problems are weakly simplicial.

Yusuke Mizota, Naoki Hamada, Shunsuke Ichiki

CoRR, 2021

Approximate Bayesian Computation of Bézier Simplices.

Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada

CoRR, 2021

2020

Topology of Pareto Sets of Strongly Convex Problems.

Naoki Hamada, Kenta Hayano, Shunsuke Ichiki, Yutaro Kabata, Hiroshi Teramoto

SIAM J. Optim., 2020

Asymptotic Risk of Bézier Simplex Fitting.

Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Neural-network assistance to calculate precise eigenvalue for fitness evaluation of real product design.

Yukito Tsunoda, Takahiro Notsu, Yasufumi Sakai, Naoki Hamada, Toshihiko Mori, Teruo Ishihara, Atsuki Inoue

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Bézier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-Objective Optimization.

Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai, Akinori Tanaka, Kenichi Bannai, Masashi Sugiyama

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Data-driven analysis of pareto set topology.

Naoki Hamada, Keisuke Goto

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

2017

Simple problems: the simplicial gluing structure of pareto sets and pareto fronts.

Naoki Hamada

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

Knee point analysis of many-objective Pareto fronts based on Reeb graph.

Naoki Hamada, Kazuhisa Chiba

Proceedings of the 2017 IEEE Congress on Evolutionary Computation, 2017

2015

Population Synthesis via k-Nearest Neighbor Crossover Kernel.

Naoki Hamada, Katsumi Homma, Hiroyuki Higuchi, Hideyuki Kikuchi

Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

2012

A new Pareto frontier covering strategy in FS-MOGA for multi-objective function optimization.

Ryo Miyazaki, Naoki Hamada, Yuichi Nagata, Isao Ono

Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems (SCIS), 2012

2011

Adaptive Weighted Aggregation 2: More scalable AWA for multiobjective function optimization.

Naoki Hamada, Yuichi Nagata, Shigenobu Kobayashi, Isao Ono

Proceedings of the IEEE Congress on Evolutionary Computation, 2011

On scalability of Adaptive Weighted Aggregation for multiobjective function optimization.

Naoki Hamada, Yuichi Nagata, Shigenobu Kobayashi, Isao Ono

Proceedings of the IEEE Congress on Evolutionary Computation, 2011

2010

Adaptive weighted aggregation: A multiobjective function optimization framework taking account of spread and evenness of approximate solutions.

Naoki Hamada, Yuichi Nagata, Shigenobu Kobayashi, Isao Ono

Proceedings of the IEEE Congress on Evolutionary Computation, 2010

2008

Functional-Specialization Multi-Objective Real-Coded Genetic Algorithm: FS-MOGA.

Naoki Hamada, Jun Sakuma, Shigenobu Kobayashi, Isao Ono

Proceedings of the Parallel Problem Solving from Nature, 2008