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

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