Kei Nishihara
Kei Nishihara
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Surrogate-assisted Evolutionary Algorithm
JPNSEC Symposium on Evolutionary Computation 2022 Report
I gave a presentation at JPNSEC Symposium on Evolutionary Computation 2022 (Hokkaido University, Sapporo, 16-18, Dec.) and got IEEE CIS Japan Chapter Young Researcher Award and Presentation Award at the same time. I was also a student member of organizers for the Open Space Discussion held as a pre event. Thank you to all those who helped make this event possible.
Kei Nishihara
Last updated on Dec 23, 2023
1 min read
Research
Surrogate-assisted Evolutionary Algorithm using Solution Update Performance as a Selection Criterion
Notice Nishihara got the IEEE CIS Young Researchers Award and Presentation Award at the same time. All materials on this page are author’s versions, not necessarily coincide with final published versions.
Kei Nishihara
,
Masaya Nakata
BibTeX
Link (Conference)
JPNSEC Symposium on Evolutionary Computation 2022
JPNSEC Symposium on Evolutionary Computation 2022
Dec 16, 2022 1:30 PM — Dec 18, 2022 6:00 PM
Hokkaido University
CFP
IEEE SSCI 2022 Report
I gave a presentation at IEEE Symposium Series on Computational Intelligence 2022 (IEEE SSCI 2022, Singapore Management University, Singapore, 4-7, Dec.). Thank you to all those who helped make this event possible.
Kei Nishihara
Last updated on Dec 23, 2023
1 min read
Research
IEEE SSCI 2022
IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022)
Dec 4, 2022 1:30 PM — Dec 7, 2022 6:00 PM
Singapore Management University, Singapore, Singapore
PDF
CFP
Surrogate-assisted Differential Evolution with Adaptation of Training Data Selection Criterion
This paper proposes a novel SAEA with adaptation of the training data selection criterion. Our proposal builds multiple RBF surrogate models with sets of training data chosen by different selection criteria, and selects one model with the best accuracy.
Kei Nishihara
,
Masaya Nakata
PDF
BibTeX
Code
Slides
DOI
Link (Paper)
Link (Conference)
Hybrid surrogate-assisted particle swarm optimization based on approximation and classification models
Notice All materials on this page are author’s versions, not necessarily coincide with final published versions.
Yushi Miyahara
,
Takumi Sonoda
,
Kei Nishihara
,
Masaya Nakata
BibTeX
Link (Conference)
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