About
Our mission is to explore and establish a methodology for evolutionary intelligence that fosters highly adaptive, autonomous, and theoretically reliable learning and optimization abilities. Our current research topics include the followings:
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Expensive optimizationMany real-world applications require optimizing expensive-to-evaluate objectives, as objective values are evaluated often with computationally expensive simulations or costly experiments. An example is the optimization of aircraft wing using CFD simulation, which is computationally expensive. To solve such expensive optimization problems, we explore sample-efficient optimization techniques. Especially, high-dimensional multi-objective problems are our main focus to be studied.
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Evolutionary machine learningIntegrating meta-heuristics into machine learning approaches bring highly adaptive and autonomous ability, such as auto-design of learning models, auto-tuning of hyper-parameters, and extraction of explainable models. We explore evolutionary machine leaning techniques; evolutionary rule-based learning, evolutionary symbolic regression, evolutionary neural architecture search, and etc. We are interested in realizing the interplay between evolution and learning on a computer intelligence scheme.
Highlights
30 Jun. 2026
We have realized an automated design of accurate and robust ensemble models via bi-objective optimization of accuracy and the degree of smoothness of approximated fitness landscapes by enabling flexible adjustment of the degree of smoothness for single-objective model-based evolutionary algorithms. This contribution has been published in IEEE Transactions on Cybernetics.
Paper
Code
7 May. 2026
We have conducted a comprehensive comparison between model-free and model-based evolutionary algorithms on high-dimensional expensive multiobjective optimization problems, suggesting that the decomposition-based approach may be overfitted to the specific shape of the feasible region. This contribution has been published in ACM Transactions on Evolutionary Learning and Optimization.
Paper
Code
15 Apr. 2025
A/Prof. Nakata has received the Young Scientists' Award, the 2025 Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology.
Contact
Our laboratory is on the eighth floor of N6-2, Hodogaya-campus, Yokohama National University. See here for more detail.
Affiliation
Faculty of Engineering, Yokohama National University
Address
Room 801/812, N6-2, Tokiwadai 79-5, Yokohama, Japan, 240-8501.
E-mail
nakata-masaya-tb at ynu.ac.jp（to Masaya Nakata)