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Nakata Lab
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A lab that realizes the adaptive capability of evolution on computers and applies it to optimization and machine learning.
AboutAbout the lab
Realizing the adaptive capability of biological evolution inside a computer
Nakata Lab explores "evolutionary intelligence," which realizes the adaptive capability of biological evolution on computers, and applies it to optimization and machine learning.
Optimization techniques may find answers that exceed existing performance, but they often require a great deal of cost and time, so the lab studies low-cost optimization.
In evolutionary machine learning, which integrates evolutionary optimization and machine learning, the lab works on automatic design of learning models, extraction of explanatory models, and more.
The lab conducts (or has conducted) collaborative research on data analysis and AI development in fields such as logistics, education, and healthcare.
Key terms
- Evolutionary intelligence
- The adaptive capability of biological evolution realized on a computer. The subject this lab explores
- Low-cost optimization
- Techniques, including Bayesian optimization, that reduce the cost and time required for optimization
- Evolutionary machine learning
- Research that integrates evolutionary optimization and machine learning to realize automatic design of learning models, extraction of explanatory models, and more
NewsLab news
A doctoral student won the FAN Presentation Award at the Intelligent System Symposium
A doctoral student won the Encouragement Award of the Japan Society for Fuzzy Theory and Intelligent Informatics
A master's student won an Encouragement Award at the Electronics, Information and Systems Society Conference of the Institute of Electrical Engineers of Japan (IEEJ)
A paper by an alumnus was accepted to IEEE Transactions on Cybernetics
A paper by a master's student was accepted to the Transactions on Mathematical Modeling and Its Applications (TOM) of the Information Processing Society of Japan
ResearchResearch themes
The lab explores evolutionary intelligence, which realizes the adaptive capability of biological evolution on computers, and applies it to optimization and machine learning. Currently, it works on low-cost optimization and evolutionary machine learning.
*Illustrative imageLow-Cost Optimization
Optimization techniques expand the possibility of discovering innovative answers, but they take considerable cost and time. The lab therefore studies techniques for reducing cost, including Bayesian optimization, and is particularly strong at problems involving multiple objective functions and many variables.
*Illustrative imageEvolutionary Machine Learning
The lab studies the integration of evolutionary optimization and machine learning to realize various functions, such as automatic design of learning models and extraction of explanatory models. It is interested in realizing the interaction between evolution and learning on computers, and also conducts theoretical research.
MembersFaculty
The members of Nakata Lab are faculty, doctoral, master's, and undergraduate students, and administrative assistants.As of 2026
- Associate ProfessorMasaya Nakata
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Island population model of an evolutionary algorithm” Studi90 / CC BY-SA 4.0 (cropped)
- Low-Cost Optimization: “Rocky-valley-maze-left” Kmtextor / CC BY-SA 4.0 (cropped)
- Evolutionary Machine Learning: “Feature Learning Diagram” Fgpacini / CC BY-SA 4.0 (cropped)
- Related lab (Optimization, Evolutionary Computation, Machine Learning, and Artificial Intelligence Lab (Kento Uchida)): “Bird Swarm - geograph.org.uk - 4777126” Andy Beecroft / CC BY-SA 2.0 (cropped)
- Related lab (Shima Lab): “EMG - SIMI” D. Gordon E. Robertson / CC BY-SA 3.0 (cropped)
- Related lab (Tanabe Lab): “Murmuration ( agrégation) d'un groupe d'étourneaux sur la sebkha de Sijoumi 3” Skander zarrad / CC BY-SA 4.0 (cropped)
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