Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation
提出一种名为AffEDA的分布估计算法，通过亲和传播聚类互信息矩阵来学习边际乘积模型，在二进制和非二进制可分解函数及HP蛋白质模型上测试，相比扩展紧凑遗传算法(ECGA)更高效，尤其在变量基数增加时结果更优。
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 (AffEDA) 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 nonbinary 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.