Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition

Adaptive operator selection (AOS) is used to determine the application rates of different operators in an online manner based on their recent performances within an optimization process. This paper proposes a bandit-based AOS method, fitness-rate-rank-based multiarmed bandit (FRRMAB). In order to tr...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 18; no. 1; pp. 114 - 130
Main Authors:	Ke Li, Fialho, Alvaro, Kwong, Sam, Qingfu Zhang
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.02.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive operator selection (AOS) Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Computer science; control theory; systems Decay Decision theory. Utility theory Decomposition Evolutionary algorithms Evolutionary computation Exact sciences and technology Finite impulse response filter Fitness Game theory Heuristic algorithms multiarmed bandit multiobjective evolutionary algorithm based on decomposition (MOEA/D) multiobjective optimization On-line systems Operational research and scientific management Operational research. Management science Operators Optimization Pareto optimization Robustness Scalars Theoretical computing Vectors Probabilistic approach Pareto optimum multiarmed bandit Process planning Evolutionary algorithm Multiobjective programming Dominance decomposition Gambling game Adaptive method Optimization multiobjective evolutionary algorithm based on decomposition (MOEA/D) multiobjective optimization Experimental result Sliding window Adaptive operator selection (AOS) Bandit process Mathematical programming
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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