A Multi-Armed Bandit Hyper-Heuristic

被引：8

作者：

Ferreira, Alexandre Silvestre ^{[1
]}

Goncalves, Richard Aderbal ^{[2
]}

Ramirez Pozo, Aurora Trinidad ^{[1
]}

机构：

[1] Univ Fed Parana, Dept Comp Sci, Curitiba, Parana, Brazil

[2] State Univ Ctr Oeste, Dept Comp Sci, Guarapuava, Brazil

来源：

2015 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS 2015) | 2015年

关键词：

Hyper-Heuristic; Multi-Armed Bandit; Combinatorial Optimization;

D O I：

10.1109/BRACIS.2015.31

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Hyper-heuristics are search methods that aim to solve optimization problems by selecting or generating heuristics. Selection hyper-heuristics choose from a pool of heuristics a good one to be applied at the current stage of the optimization process. The selection mechanism is the main part of a selection hyperheuristic and have a great impact on its performance. In this paper a deterministic selection mechanism based on the concepts of the Multi-Armed Bandit (MAB) problem is proposed. The proposed approach is integrated into the HyFlex framework and is compared to twenty other hyper-heuristics using the methodology adapted by the CHeSC 2011 Challenge. The results obtained were good and comparable to those attained by the best hyper-heuristics. Therefore, it is possible to affirm that the use of a MAB mechanism as a selection method in a hyper-heuristic is a promising approach.

引用

页码：13 / 18

页数：6

共 50 条

[41] CHARACTERIZING TRUTHFUL MULTI-ARMED BANDIT MECHANISMS
Babaioff, Moshe
Sharma, Yogeshwer
Slivkins, Aleksandrs
SIAM JOURNAL ON COMPUTING, 2014, 43 (01) : 194 - 230
[42] Robust control of the multi-armed bandit problem
Felipe Caro
Aparupa Das Gupta
Annals of Operations Research, 2022, 317 : 461 - 480
[43] Multi-armed bandit problem with known trend
Bouneffouf, Djallel
Feraud, Raphael
NEUROCOMPUTING, 2016, 205 : 16 - 21
[44] Variational inference for the multi-armed contextual bandit
Urteaga, Inigo
Wiggins, Chris H.
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 84, 2018, 84
[45] Ambiguity aversion in multi-armed bandit problems
Christopher M. Anderson
Theory and Decision, 2012, 72 : 15 - 33
[46] Bridging Adversarial and Nonstationary Multi-Armed Bandit
Chen, Ningyuan
Yang, Shuoguang
Zhang, Hailun
PRODUCTION AND OPERATIONS MANAGEMENT, 2025,
[47] An Incentive-Compatible Multi-Armed Bandit Mechanism
Gonen, Rica
Pavlov, Elan
PODC'07: PROCEEDINGS OF THE 26TH ANNUAL ACM SYMPOSIUM ON PRINCIPLES OF DISTRIBUTED COMPUTING, 2007, : 362 - 363
[48] Achieving Fairness in the Stochastic Multi-Armed Bandit Problem
Patil, Vishakha
Ghalme, Ganesh
Nair, Vineet
Narahari, Y.
JOURNAL OF MACHINE LEARNING RESEARCH, 2021, 22
[49] Gaussian multi-armed bandit problems with multiple objectives
Reverdy, Paul
2016 AMERICAN CONTROL CONFERENCE (ACC), 2016, : 5263 - 5269
[50] Adaptive Active Learning as a Multi-armed Bandit Problem
Czarnecki, Wojciech M.
Podolak, Igor T.
21ST EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2014), 2014, 263 : 989 - 990

← 1 2 3 4 5 →