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Standard genetic algorithms are not very suited to problems with multivariate interactions among variables. This problem has been identified from the beginning of these algorithms and has been termed as the linkage learning problem. Numerous attempts have been carried out to solve this problem with various degree of success. In this paper, we employ an effective algorithm to cluster a dependency structure matrix (DSM) which can correctly identify the linkage groups. Once all the linkage groups are identified, a simple genetic algorithm using BB-wise crossover can easily solve hard optimization problems. Experimental results with a number of deceptive functions with various sizes presented to show the efficiency enhancement obtained by the proposed method. The results are also compared with Bayesian Optimization Algorithm, a well-known evolutionary optimizer, to demonstrate this improvement.