The impact of the bin packing problem structure in hyper-heuristic performance

Authors:
Eunice López-Camacho;Hugo Terashima-Marín;Santiago Enrique Conant-Pablos
Affiliations:
ITESM, Monterrey, Mexico;ITESM, Monterrey, Mexico;ITESM, Monterrey, Mexico
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

Citing 3
Cited 0

A Knowledge Discovery Approach to Understanding Relationships between Scheduling Problem Structure and Heuristic Performance

Learning and Intelligent Optimization
Defining a problem-state representation with data mining within a hyper-heuristic model which solves 2D irregular bin packing problems

IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
A hyper-heuristic for solving one and two-dimensional bin packing problems

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

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Abstract

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 Component 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.