Kei Nishihara
Kei Nishihara
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Differential Evolution
PPSN 2024
International Conference on Parallel Problem Solving from Nature (PPSN 2024)
Sep 14, 2024 9:00 AM — Sep 18, 2024 6:00 PM
University of Applied Sciences Upper Austria, Hagenberg im Mühlkreis, Austria
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CFP
A Surrogate-assisted Partial Optimization for Expensive Constrained Optimization Problems
This work proposes a new surrogate-assisted evolutionary algorithm that partially optimizes each objective/constraint, namely surrogate-assisted partial optimization (SAPO). Solutions with better values of objective/constraint are selected from the evaluated solutions as the parent solutions and a focused objective/constraint is independently optimized using surrogate models one by one.
Kei Nishihara
,
Masaya Nakata
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BibTeX
Code
Poster
DOI
Link (Paper)
Link (Conference)
Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems
This paper presents a novel surrogate-assisted evolutionary algorithm based on scalarization function approximation, which is designed to strengthen its robustness against this deterioration. The proposed algorithm, called SFA/DE, constructs an approximation model for each scalarization function defined in a decomposition-based framework. Each decomposed problem is then solved using multiple independent models trained for its neighbor problems.
Yuma Horaguchi
,
Kei Nishihara
,
Masaya Nakata
BibTeX
Code
DOI
Link
JPNSEC Symposium on Evolutionary Computation 2023 Report
I gave a presentation at JPNSEC Symposium on Evolutionary Computation 2023 (Ohoribata Convention Hall, Odawara, 21-23, Dec.). I was also a student member of organizers for the Open Space Discussion. Thank you to all those who helped make this event possible.
Kei Nishihara
Last updated on Jan 2, 2024
1 min read
Research
Surrogate-assisted Evolutionary Algorithm using Pareto-optimal Surrogates Set
Notice 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 2023
JPNSEC Symposium on Evolutionary Computation 2023
Dec 21, 2023 1:30 PM — Dec 23, 2023 6:00 PM
Odawara Ohoribata Convention Hall
CFP
GECCO 2023
The Genetic and Evolutionary Computation Conference (GECCO 2023)
Jul 15, 2023 12:00 PM — Jul 19, 2023 6:00 PM
Altis Grand Hotel, Lisbon, Portugal
PDF
CFP
ACM GECCO 2023 Report
I gave a poster presentation at The Genetic and Evolutionary Computation Conference (GECCO 2023, Altis Grand Hotel, Lisbon, Portugal, 15-19, Jul). Thank you to all those who helped make this event possible.
Kei Nishihara
Last updated on Dec 23, 2023
1 min read
Research
Utilizing the Expected Gradient in Surrogate-assisted Evolutionary Algorithms
This paper utilizes the expected gradient of the Gaussian Process (GP) in a surrogate-assisted evolutionary algorithm. Specifically, our proposal iteratively runs a quasi-Newton method (L-BFGS-B) changing initial points on multiple GPs constructed to approximate the promising region of the objective function.
Kei Nishihara
,
Masaya Nakata
PDF
BibTeX
Poster
Slides
DOI
Link (Paper)
Link (Conference)
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
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