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Information Systems Design Lab
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A lab that takes on large-scale computation and optimization with evolutionary computation and the cloud.
AboutAbout the lab
Optimization and the cloud, learning from how evolution works.
The Information Systems Design Lab studies optimization algorithms that use evolutionary computation and deep learning, as well as cloud computing.
It covers a wide range of themes, including security and large-scale distributed parallel processing.
The Information Initiative Center builds and provides a large-scale distributed academic cloud for researchers across Japan, and the lab draws on that experience in its research.
Key terms
- Evolutionary computation
- A computational method that imitates biological evolution, searching for an answer by selecting and combining candidate solutions
- Genetic algorithm
- A representative evolutionary computation method that treats candidate solutions as genes and repeats crossover and mutation to search for better solutions
- Surrogate model
- A model that quickly approximates and predicts the results of time-consuming computations or experiments
ResearchResearch themes
The lab researches optimization algorithms using evolutionary computation and deep learning, cloud computing, security, and large-scale distributed parallel processing. It also draws on the experience of the Information Initiative Center, which built the Hokkaido University High Performance Intercloud and has provided it to researchers across Japan.
*Illustrative imageCloud Computing
The Information Initiative Center has built a large-scale distributed academic cloud system, the Hokkaido University High Performance Intercloud, and provides it to researchers across Japan. Drawing on this experience, the lab conducts research on cloud computing.
*Illustrative imageLarge-Scale Parallelization of Evolutionary Computation
In advanced evolutionary computation methods, the "gene analysis" step, such as linkage identification, is costly, and parallelizing it is a challenge. For the multi-core and many-core era, the lab researches and develops large-scale parallelization of advanced evolutionary computation methods.
*Illustrative imageLinkage Identification in Genetic Algorithms
The lab develops linkage identification methods for genetic algorithms, which find combinations of gene loci that can form the components of good solutions (building blocks).
*Illustrative imageOptimization Using Surrogate Models
For problems where evaluating the objective function requires long simulations or experiments, computation time must be reduced with low-cost surrogate models. The lab researches evolutionary computation algorithms that use surrogate models such as neural networks.
MembersFaculty
The members of the Information Systems Design Lab are faculty and students.As of 2023
- ProfessorMasaharu Munetomo
- Associate ProfessorAkiyoshi Sugiki
VoicesStudent voices
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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: “Adaptive landscape” Rhiever / CC BY 3.0 (cropped)
- Cloud Computing: “Datacenter Server Racks ( )” Carl Lender from Sunrise, USA / CC BY 2.0 (cropped)
- Large-Scale Parallelization of Evolutionary Computation: “BalticServers data center” BalticServers.com / CC BY-SA 3.0 (cropped)
- Linkage Identification in Genetic Algorithms: “Crossing-over-meiosis” Science Artist / CC0 (cropped)
- Optimization Using Surrogate Models: “Artificial neural network colored” Phlsph7 / CC BY-SA 3.0 (cropped)
- Related lab (Autonomous Systems Engineering Lab): “Kilobot robot swarm” asuscreative / CC BY-SA 4.0 (cropped)
- Related lab (Nano Material Properties Engineering Lab): “Stretchable Electronics” Steve Jurvetson from Menlo Park, USA / CC BY 2.0 (cropped)
- Related lab (Information Media Environment Lab): “Global illumination” user:Gtanski / Public domain (cropped)
- Related lab (Intelligent Informatics Lab): “Quiet Electric Engine V1 (QUEEN V1) Experiment (GRC-2024-C-06530)” NASA Glenn Research Center / NASA/GRC/Sara Lowthian-Hanna / Public domain (cropped)
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