Hiroya Makino

A researcher in the area of Computer Science; Multi-Agent Path Planning 🤖, Evolutionary Computing 🐜, Artificial Intelligence 💻

Publications

🐜 Makino, H., & Kita, E. (2024).
Stochastic Schemata Exploiter-Based Optimization of Hyper-parameters for XGBoost.
Computer Assisted Methods in Engineering and Science, 31(1), 113-132. [link]
🐜 Makino, H., & Kita, E. (2023).
Application of a Stochastic Schemata Exploiter for Multi-Objective Hyper-parameter Optimization of Machine Learning.
The Review of Socionetwork Strategies, 17(2), 179-213. [link]

💻 Makino, H., Yamaguchi, T., Sakai, H. (2025).
Zero-Shot Visual Concept Blending Without Text Guidance.
Under Review. [arXiv, github]
💻 Oishi, K., Kato, T., Makino, H., Ito, S. (2025).
Visual-Based Forklift Learning System Enabling Zero-Shot Sim2Real Without Real-World Data.
In 2025 IEEE International Conference on Robotics and Automation (ICRA). [arXiv]
🤖 Makino, H., & Ito, S. (2024).
Online Multi-Agent Pickup and Delivery with Task Deadlines.
In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). [oral accepted, link, arXiv]
🤖 Makino, H., Ohama, Y., & Ito, S. (2024).
MARPF: Multi-Agent and Multi-Rack Path Finding.
In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). [oral accepted, link, arXiv, project page (animation available on desktop)]
🐜 Takai, A., Makino, H., & Kita, E. (2023).
SSE-Based Evolutionary Algorithm for Hyper-parameter Optimization of LightGBM on Paddy Rice Yield Prediction Problem.
In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4375-4380. [link]
🐜 Makino, H., & Kita, E. (2021).
Stochastic Schemata Exploiter-Based AutoML.
In 2021 International Conference on Data Mining Workshops (ICDMW), 238-245. [link, github]

💻 Fujita, M., Yamada, A., Susuki, M., Makino, H. & Kita, E. (2021).
Application of Machine Learning for Growth Environment Prediction in Agriculture.
In 2021 International Conference on Data Mining Workshops (ICDMW), 208-213. [link]
🐜 Makino, H., Feng, X., & Kita, E. (2020).
Stochastic Schemata Exploiter-Based Optimization of Convolutional Neural Network.
In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4365-4371. [link]

2016/4-2020/3 School of Informatics and Sciences, Nagoya University (Japan), top graduate
2020/4-2022/3 Graduate School of Informatics, Nagoya University (Japan)

Technical support for online learning support system (NUCT) under COVID-19
Develop a restaurant ordering application customized for each individual
Teaching assistant and development of new courses in an AI education startup (SkillUp AI)
Development of recommendation algorithm for video streaming service (Locipo)

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