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.
|Title of host publication||2008 IEEE World Congress on Computational Intelligence|
|Place of Publication||Hong Kong|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Pages||4118 - 4124|
|Number of pages||7|
|Publication status||Published - 2008|
|Event||IEEE International Joint Conference on Neural Networks 2008 - Hong Kong Convention and Exhibition Centre, Hong Kong|
Duration: 1 Jan 2008 → …
|Conference||IEEE International Joint Conference on Neural Networks 2008|
|Abbreviated title||IJCNN 2008|
|Period||1/01/08 → …|
Smith-Miles, K. A. (2008). Towards insightful algorithm selection for optimisation using meta-learning concepts. In D. Liu (Ed.), 2008 IEEE World Congress on Computational Intelligence (pp. 4118 - 4124). IEEE, Institute of Electrical and Electronics Engineers.