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The hessian estimation evolution strategy. / Glasmachers, Tobias; Krause, Oswin.
Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Proceedings. red. / Thomas Bäck; Mike Preuss; André Deutz; Michael Emmerich; Hao Wang; Carola Doerr; Heike Trautmann. Springer, 2020. s. 597-609 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12269 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Glasmachers, T
& Krause, O 2020,
The hessian estimation evolution strategy. i T Bäck, M Preuss, A Deutz, M Emmerich, H Wang, C Doerr & H Trautmann (red),
Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 12269 LNCS, s. 597-609, 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, Leiden, Holland,
05/09/2020.
https://doi.org/10.1007/978-3-030-58112-1_41APA
Glasmachers, T.
, & Krause, O. (2020).
The hessian estimation evolution strategy. I T. Bäck, M. Preuss, A. Deutz, M. Emmerich, H. Wang, C. Doerr, & H. Trautmann (red.),
Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Proceedings (s. 597-609). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 12269 LNCS
https://doi.org/10.1007/978-3-030-58112-1_41Vancouver
Glasmachers T
, Krause O.
The hessian estimation evolution strategy. I Bäck T, Preuss M, Deutz A, Emmerich M, Wang H, Doerr C, Trautmann H, red., Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Proceedings. Springer. 2020. s. 597-609. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12269 LNCS).
https://doi.org/10.1007/978-3-030-58112-1_41Author
Glasmachers, Tobias ; Krause, Oswin. / The hessian estimation evolution strategy. Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Proceedings. red. / Thomas Bäck ; Mike Preuss ; André Deutz ; Michael Emmerich ; Hao Wang ; Carola Doerr ; Heike Trautmann. Springer, 2020. s. 597-609 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12269 LNCS).
Bibtex
@inproceedings{5e668176fbf14f32bd7665f2e57f25de,
title = "The hessian estimation evolution strategy",
abstract = "We present a novel black box optimization algorithm called Hessian Estimation Evolution Strategy. The algorithm updates the covariance matrix of its sampling distribution by directly estimating the curvature of the objective function. This algorithm design is targeted at twice continuously differentiable problems. For this, we extend the cumulative step-size adaptation algorithm of the CMA-ES to mirrored sampling. We demonstrate that our approach to covariance matrix adaptation is efficient by evaluating it on the BBOB/COCO testbed. We also show that the algorithm is surprisingly robust when its core assumption of a twice continuously differentiable objective function is violated. The approach yields a new evolution strategy with competitive performance, and at the same time it also offers an interesting alternative to the usual covariance matrix update mechanism.",
keywords = "Covariance matrix adaptation, Evolution strategy, Hessian matrix",
author = "Tobias Glasmachers and Oswin Krause",
year = "2020",
doi = "10.1007/978-3-030-58112-1_41",
language = "English",
isbn = "9783030581114",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "597--609",
editor = "Thomas B{\"a}ck and Mike Preuss and Andr{\'e} Deutz and Michael Emmerich and Hao Wang and Carola Doerr and Heike Trautmann",
booktitle = "Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Proceedings",
address = "Switzerland",
note = "16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 ; Conference date: 05-09-2020 Through 09-09-2020",
}
RIS
TY - GEN
T1 - The hessian estimation evolution strategy
AU - Glasmachers, Tobias
AU - Krause, Oswin
PY - 2020
Y1 - 2020
N2 - We present a novel black box optimization algorithm called Hessian Estimation Evolution Strategy. The algorithm updates the covariance matrix of its sampling distribution by directly estimating the curvature of the objective function. This algorithm design is targeted at twice continuously differentiable problems. For this, we extend the cumulative step-size adaptation algorithm of the CMA-ES to mirrored sampling. We demonstrate that our approach to covariance matrix adaptation is efficient by evaluating it on the BBOB/COCO testbed. We also show that the algorithm is surprisingly robust when its core assumption of a twice continuously differentiable objective function is violated. The approach yields a new evolution strategy with competitive performance, and at the same time it also offers an interesting alternative to the usual covariance matrix update mechanism.
AB - We present a novel black box optimization algorithm called Hessian Estimation Evolution Strategy. The algorithm updates the covariance matrix of its sampling distribution by directly estimating the curvature of the objective function. This algorithm design is targeted at twice continuously differentiable problems. For this, we extend the cumulative step-size adaptation algorithm of the CMA-ES to mirrored sampling. We demonstrate that our approach to covariance matrix adaptation is efficient by evaluating it on the BBOB/COCO testbed. We also show that the algorithm is surprisingly robust when its core assumption of a twice continuously differentiable objective function is violated. The approach yields a new evolution strategy with competitive performance, and at the same time it also offers an interesting alternative to the usual covariance matrix update mechanism.
KW - Covariance matrix adaptation
KW - Evolution strategy
KW - Hessian matrix
U2 - 10.1007/978-3-030-58112-1_41
DO - 10.1007/978-3-030-58112-1_41
M3 - Article in proceedings
AN - SCOPUS:85091292421
SN - 9783030581114
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 597
EP - 609
BT - Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Proceedings
A2 - Bäck, Thomas
A2 - Preuss, Mike
A2 - Deutz, André
A2 - Emmerich, Michael
A2 - Wang, Hao
A2 - Doerr, Carola
A2 - Trautmann, Heike
PB - Springer
T2 - 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020
Y2 - 5 September 2020 through 9 September 2020
ER -
