Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A heuristically perturbation of dataset to achieve a diverse ensemble of classifiers
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Unsupervised linkage learner based on local optimums
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
A heuristic diversity production approach
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
A clustering ensemble based on a modified normalized mutual information metric
AMT'12 Proceedings of the 8th international conference on Active Media Technology
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Genetic Algorithms (GAs) are categorized as search heuristics and have been broadly applied to optimization problems. These algorithms have been used for solving problems in many applications, but it has been shown that simple GA is not able to effectively solve complex real world problems. For proper solving of such problems, knowing the relationships between decision variables which is referred to as linkage learning is necessary. In this paper a linkage learning approach is proposed that utilizes the special features of the decomposable problems to solve them. The proposed approach is called Local Optimums based Linkage Learner (LOLL). The LOLL algorithm is capable of identifying the groups of variables which are related to each other (known as linkage groups), no matter if these groups are overlapped or different in size. The proposed algorithm, unlike other linkage learning techniques, is not done along with optimization algorithm; but it is done in a whole separated phase from optimization search. After finding linkage group information by LOLL, an optimization search can use this information to solve the problem. LOLL is tested on some benchmarked decomposable functions. The results show that the algorithm is an efficient alternative to other linkage learning techniques.