Surrogate-assisted Evolutionary Algorithm

The Genetic and Evolutionary Computation Conference (GECCO 2023)

I gave a poster presentation at The Genetic and Evolutionary Computation Conference 2024 (GECCO 2023, Altis Grand Hotel, Lisbon, Portugal, 15-19, Jul). Thank you to all those who helped make this event possible.

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.

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.

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.

JPNSEC Symposium on Evolutionary Computation 2022

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.

IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022)

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.

Notice All materials on this page are author’s versions, not necessarily coincide with final published versions.