An Asynchronous Implementation of the Limited Memory CMA-ES

Viktor Arkhipov; Maxim Buzdalov; A. A. Shalyto

doi:10.1109/ICMLA.2015.97

An Asynchronous Implementation of the Limited Memory CMA-ES

Viktor Arkhipov, Maxim Buzdalov, A. A. Shalyto

ITMO University

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (ISBN)

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We present our asynchronous implementation of the LM-CMA-ES algorithm, which is a modern evolution strategy for solving complex large-scale continuous optimization problems. Our implementation brings the best results when the number of cores is relatively high and the computational complexity of the fitness function is also high. The experiments with benchmark functions show that it is able to overcome its origin on the Sphere function, reaches certain thresholds faster on the Rosenbrock and Ellipsoid function, and surprisingly performs much better than the original version on the Rastrigin function.

Iaith wreiddiol	Saesneg
Teitl	2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)
Cyhoeddwr	IEEE Press
Tudalennau	707-712
Nifer y tudalennau	6
ISBN (Electronig)	978-1-5090-0287-0, 1509002871
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/ICMLA.2015.97
Statws	Cyhoeddwyd - 03 Maw 2016
Cyhoeddwyd yn allanol	Ie
Digwyddiad	IEEE 14th International Conference on Machine Learning and Applications (ICMLA) - Miami, Unol Daleithiau America Hyd: 09 Rhag 2015 → 11 Rhag 2015

Cynhadledd

Cynhadledd	IEEE 14th International Conference on Machine Learning and Applications (ICMLA)
Gwlad/Tiriogaeth	Unol Daleithiau America
Dinas	Miami
Cyfnod	09 Rhag 2015 → 11 Rhag 2015

Mynediad at Ddogfen

10.1109/ICMLA.2015.97

http://arxiv.org/abs/1510.00419

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https://www.scopus.com/pages/publications/84969718862

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@inproceedings{551378e1e30842cc83a953cc7253c2e2,

title = "An Asynchronous Implementation of the Limited Memory CMA-ES",

abstract = " We present our asynchronous implementation of the LM-CMA-ES algorithm, which is a modern evolution strategy for solving complex large-scale continuous optimization problems. Our implementation brings the best results when the number of cores is relatively high and the computational complexity of the fitness function is also high. The experiments with benchmark functions show that it is able to overcome its origin on the Sphere function, reaches certain thresholds faster on the Rosenbrock and Ellipsoid function, and surprisingly performs much better than the original version on the Rastrigin function.",

keywords = "CMA-ES, asynchronous algorithms, parallel algorithms",

author = "Viktor Arkhipov and Maxim Buzdalov and Shalyto, \{A. A.\}",

year = "2016",

month = mar,

day = "3",

doi = "10.1109/ICMLA.2015.97",

language = "English",

pages = "707--712",

booktitle = "2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)",

publisher = "IEEE Press",

address = "United States of America",

note = "IEEE 14th International Conference on Machine Learning and Applications (ICMLA) ; Conference date: 09-12-2015 Through 11-12-2015",

}

Arkhipov, V, Buzdalov, M & Shalyto, AA 2016, An Asynchronous Implementation of the Limited Memory CMA-ES. yn 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE Press, tt. 707-712, IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, Fflorida, Unol Daleithiau America, 09 Rhag 2015. https://doi.org/10.1109/ICMLA.2015.97

TY - GEN

T1 - An Asynchronous Implementation of the Limited Memory CMA-ES

AU - Arkhipov, Viktor

AU - Buzdalov, Maxim

AU - Shalyto, A. A.

PY - 2016/3/3

Y1 - 2016/3/3

N2 - We present our asynchronous implementation of the LM-CMA-ES algorithm, which is a modern evolution strategy for solving complex large-scale continuous optimization problems. Our implementation brings the best results when the number of cores is relatively high and the computational complexity of the fitness function is also high. The experiments with benchmark functions show that it is able to overcome its origin on the Sphere function, reaches certain thresholds faster on the Rosenbrock and Ellipsoid function, and surprisingly performs much better than the original version on the Rastrigin function.

AB - We present our asynchronous implementation of the LM-CMA-ES algorithm, which is a modern evolution strategy for solving complex large-scale continuous optimization problems. Our implementation brings the best results when the number of cores is relatively high and the computational complexity of the fitness function is also high. The experiments with benchmark functions show that it is able to overcome its origin on the Sphere function, reaches certain thresholds faster on the Rosenbrock and Ellipsoid function, and surprisingly performs much better than the original version on the Rastrigin function.

KW - CMA-ES

KW - asynchronous algorithms

KW - parallel algorithms

UR - https://www.scopus.com/pages/publications/84969718862

U2 - 10.1109/ICMLA.2015.97

DO - 10.1109/ICMLA.2015.97

M3 - Conference Proceeding (ISBN)

SP - 707

EP - 712

BT - 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

PB - IEEE Press

T2 - IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

Y2 - 9 December 2015 through 11 December 2015

ER -

An Asynchronous Implementation of the Limited Memory CMA-ES

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