Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning

Arina Buzdalova; Maxim Buzdalov

doi:10.1109/ICMLA.2012.32

Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning

Arina Buzdalova, Maxim Buzdalov

ITMO University

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

29 Dyfyniadau (Scopus)

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In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fitness functions. The requirements for this method are formulated. The compliance of the method with these requirements is illustrated on model problems such as Royal Roads problem and H-IFF optimization problem. The experiments confirm that the method increases the efficiency of evolutionary algorithms.

Iaith wreiddiol	Saesneg
Teitl	ICMLA '12
Is-deitl	Proceedings of the 2012 11th International Conference on Machine Learning and Applications
Cyhoeddwr	IEEE Press
Tudalennau	150-155
Nifer y tudalennau	6
Cyfrol	1
ISBN (Argraffiad)	978-1-4673-4651-1
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/ICMLA.2012.32
Statws	Cyhoeddwyd - 12 Rhag 2012
Cyhoeddwyd yn allanol	Ie

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10.1109/ICMLA.2012.32

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https://www.scopus.com/record/display.uri?eid=2-s2.0-84873587026&origin=resultslist

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@inproceedings{216be38118aa4c86af4ea5d1725b41a7,

title = "Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning",

abstract = "In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fitness functions. The requirements for this method are formulated. The compliance of the method with these requirements is illustrated on model problems such as Royal Roads problem and H-IFF optimization problem. The experiments confirm that the method increases the efficiency of evolutionary algorithms.",

author = "Arina Buzdalova and Maxim Buzdalov",

year = "2012",

month = dec,

day = "12",

doi = "10.1109/ICMLA.2012.32",

language = "English",

isbn = "978-1-4673-4651-1",

volume = "1",

pages = "150--155",

booktitle = "ICMLA '12",

publisher = "IEEE Press",

address = "United States of America",

}

Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning. / Buzdalova, Arina ; Buzdalov, Maxim.
ICMLA '12: Proceedings of the 2012 11th International Conference on Machine Learning and Applications. Cyfrol 1 IEEE Press, 2012. t. 150-155.

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

TY - GEN

T1 - Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning

AU - Buzdalova, Arina

AU - Buzdalov, Maxim

PY - 2012/12/12

Y1 - 2012/12/12

N2 - In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fitness functions. The requirements for this method are formulated. The compliance of the method with these requirements is illustrated on model problems such as Royal Roads problem and H-IFF optimization problem. The experiments confirm that the method increases the efficiency of evolutionary algorithms.

AB - In this paper further investigation of the previously proposed method of speeding up single-objective evolutionary algorithms is done. The method is based on reinforcement learning which is used to choose auxiliary fitness functions. The requirements for this method are formulated. The compliance of the method with these requirements is illustrated on model problems such as Royal Roads problem and H-IFF optimization problem. The experiments confirm that the method increases the efficiency of evolutionary algorithms.

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DO - 10.1109/ICMLA.2012.32

M3 - Conference Proceeding (Non-Journal item)

SN - 978-1-4673-4651-1

VL - 1

SP - 150

EP - 155

BT - ICMLA '12

PB - IEEE Press

ER -

Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning

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