Unsupervised Machine Learning for the Quadratic Assignment Problem

被引：0

作者：

The Van Luong ^{[1
]}

Taillard, Eric D. ^{[2
]}

机构：

[1] Univ Lausanne, Serv Rech, Batiment Amphipole, CH-1015 Lausanne, Switzerland

[2] Univ Appl Sci & Arts Western Switzerland, Dept Informat & Commun Technol, HEIG VD, Route Cheseaux 1, CH-1401 Yverdon, Switzerland

来源：

METAHEURISTICS, MIC 2022 | 2023年 / 13838卷

关键词：

Machine learning; Big data; Metaheuristics; Quadratic assignment;

D O I：

10.1007/978-3-031-26504-4_9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An unsupervised machine learning method based on association rule is studied for the Quadratic Assignment Problem. Parallel extraction of itemsets and local search algorithms are proposed. The extraction of frequent itemsets in the context of local search is shown to produce good results for a few problem instances. Negative results of the proposed learning mechanism are reported for other instances. This result contrasts with other hard optimization problems for which efficient learning processes are known in the context of local search.

引用

页码：118 / 132

页数：15

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