The Impact of the Bin Packing Problem Structure in Hyper-heuristic Performance

被引：0

作者：

Lopez-Camacho, Eunice ^{[1
]}

Terashima-Marin, Hugo ^{[1
]}

Enrique Conant-Pablos, Santiago ^{[1
]}

机构：

[1] ITESM Intelligent Syst, Av E Garza Sada 2501, Monterrey 64849, NL, Mexico

来源：

PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTATION COMPANION (GECCO'12) | 2012年

关键词：

Bin Packing; 2D irregular Bin Packing Problem; Optimization; Heuristics; Hyper-hcuristics; Principal Component Analysis;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We use a knowledge discovery approach to get insights over the features of the bin packing problem and its relationship in the performance of an evolutionary-based model of hyper-heuristics. The evolutionary model produces rules that combine the application of up to six different low-level heuristics during the solution of a given problem instance. Using the Principal Coiponent Analysis (PCA) method, we visualize in two dimensions all instances characterized by a larger number of features. By over imposing features and hyper-heuristic performance over the 2D graphs, it is possible to draw conclusions about the relation between the bin packing problem structure and the hyper-heuristics performance.

引用

页码：1545 / 1546

页数：2

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