Sciweavers

Share
EC
2010

Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation

8 years 8 months ago
Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation
Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of, mainly binary, optimization problems. In this paper, we introduce the affinity propagation EDA which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and non-binary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased.
Roberto Santana, Pedro Larrañaga, Jos&eacut
Added 02 Mar 2011
Updated 02 Mar 2011
Type Journal
Year 2010
Where EC
Authors Roberto Santana, Pedro Larrañaga, José Antonio Lozano
Comments (0)
books