Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
A new N-gram feature extraction-selection method for malicious code
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
A novel classifier ensemble method based on class weightening in huge dataset
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
An innovative feature selection using fuzzy entropy
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
An innovative linkage learning based on differences in local optimums
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
A new clustering algorithm with the convergence proof
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Linkage learning based on local optima
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Detection of cancer patients using an innovative method for learning at imbalanced datasets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A metric to evaluate a cluster by eliminating effect of complement cluster
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
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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 Linkage Learner based on Local Optimums and Clustering (LLLC). The LLLC 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 LLLC, an optimization search can use this information to solve the problem. LLLC is tested on some benchmarked decomposable functions. The results show that the algorithm is an efficient alternative to other linkage learning techniques.