Výsledky vyhledávání - "Arkadiy Dushatskiy"

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

Autor: Tanja Alderliesten, Peter A. N. Bosman, Arkadiy Dushatskiy

Publikováno v: ACM Transactions on Evolutionary Learning and Optimization. 1:1-23

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3cd3e87bc759456093cfefd7daf51eb4
https://doi.org/10.1145/3453141

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A novel surrogate-assisted evolutionary algorithm applied to partition-based ensemble learning

Autor: Peter A. N. Bosman, Tanja Alderliesten, Arkadiy Dushatskiy

Publikováno v: GECCO 2021-Proceedings of the 2021 Genetic and Evolutionary Computation Conference, 583-591
STARTPAGE=583;ENDPAGE=591;TITLE=GECCO 2021-Proceedings of the 2021 Genetic and Evolutionary Computation Conference
GECCO
GECCO 2021-Proceedings of the 2021 Genetic and Evolutionary Computation Conference

We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Gene-Po

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::414d980b368d72d34b05a798ca942ede
http://www.scopus.com/inward/record.url?scp=85110096162&partnerID=8YFLogxK

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Convolutional neural network surrogate-assisted GOMEA

Autor: Peter A. N. Bosman, Adriënne M. Mendrik, Tanja Alderliesten, Arkadiy Dushatskiy

Publikováno v: GECCO
GECCO 2019-Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 753-761
STARTPAGE=753;ENDPAGE=761;TITLE=GECCO 2019-Proceedings of the 2019 Genetic and Evolutionary Computation Conference

We introduce a novel surrogate-assisted Genetic Algorithm (GA) for expensive optimization of problems with discrete categorical variables. Specifically, we leverage the strengths of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA), a state

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55de33ddd6129a166eb055839f12128b
https://ir.cwi.nl/pub/28897

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