A New Algorithm for Adaptive Online Selection of Auxiliary Objectives

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

Crynodeb

Consider optimization problems, where a target objective should be optimized. Some auxiliary objectives can be used to obtain the optimum of the target objective in less number of objective evaluations. We call such auxiliary objective a supporting one. Usually there is no prior knowledge about properties of auxiliary objectives, some objectives can be obstructive as well. What is more, an auxiliary objective can be both supporting and obstructive at different stages of the target objective optimization. Thus, an adaptive online method of objective selection is needed. Earlier, we proposed a method for doing that, which is based on reinforcement learning. In this paper, a new algorithm for adaptive online selection of optimization objectives is proposed. The algorithm meets the interface of a reinforcement learning agent, so it can be fit into the previously proposed framework. The new algorithm is applied for solving some benchmark problems with single-objective evolutionary algorithms. Specifically, Leading Ones with OneMax auxiliary objective is considered, as well as the MH-IFF problem. Experimental results are presented. The proposed algorithm outperforms Q-learning and random objective selection on the considered problems.
Iaith wreiddiolSaesneg
TeitlICMLA '14
Is-deitlProceedings of the 2014 13th International Conference on Machine Learning and Applications
GolygyddionC. Ferri, G. Qu, X. Chen, M. A. Wani, P. Angelov, J.-H Lai
CyhoeddwrIEEE Press
Tudalennau584-587
Nifer y tudalennau4
ISBN (Electronig)978-1-4799-7415-3
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 03 Rhag 2014
Cyhoeddwyd yn allanolIe
Digwyddiad2014 13th International Conference on Machine Learning and Applications (ICMLA) - Detroit, Unol Daleithiau America
Hyd: 03 Rhag 201406 Rhag 2014

Cynhadledd

Cynhadledd2014 13th International Conference on Machine Learning and Applications (ICMLA)
Gwlad/TiriogaethUnol Daleithiau America
DinasDetroit
Cyfnod03 Rhag 201406 Rhag 2014

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