Abstract
In this paper we propose a meta-learning inspired framework for analysing the performance of meta-heuristics for optimization problems, and developing insights into the relationships between search space characteristics of the problem instances and algorithm performance. Preliminary results based on several meta-heuristics for well-known instances of the Quadratic Assignment Problem are presented to illustrate the approach using both supervised and unsupervised learning methods.
| Original language | English |
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| Title of host publication | IEEE International Joint Conference on Neural Networks 2008 |
| Editors | Derong Liu |
| Place of Publication | Hong Kong |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 4118 - 4124 |
| Number of pages | 7 |
| ISBN (Print) | 978-1-4244-1820-6 |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | IEEE International Joint Conference on Neural Networks 2008 - Hong Kong Convention and Exhibition Centre, Hong Kong, China Duration: 1 Jun 2008 → 8 Jun 2008 https://ieeexplore.ieee.org/xpl/conhome/4625775/proceeding (Proceedings) |
Conference
| Conference | IEEE International Joint Conference on Neural Networks 2008 |
|---|---|
| Abbreviated title | IJCNN 2008 |
| Country/Territory | China |
| City | Hong Kong |
| Period | 1/06/08 → 8/06/08 |
| Internet address |