Towards insightful algorithm selection for optimisation using meta-learning concepts
Contribuinte(s) |
Wang, Jun |
---|---|
Data(s) |
01/01/2008
|
Resumo |
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.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30018286/smithmiles-towardsinsightful-2008.pdf http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4634391&isnumber=4633757 |
Direitos |
2008, IEEE |
Tipo |
Conference Paper |