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Most of optimization problems in various fields are in NP-class. This implies that the time to find the optimum solution of any problem is obviously non-polynomial. Although the development of high speed computer architectures and the concept parallel computing is practically successful, some of these problems are constrained by the problem of tight data dependency which prevents the possibility of deploying a parallel architecture as well as processing to solve the problem. Evolutionary algorithms which are based on guessing solutions have been developed to find an acceptable solution in a short time. However, the processing time based on guessing to achieve the acceptable solution is unpredictable and uncontrollable. In this paper, we compare the guessing process in some popular algorithms to define a generic structure of searching process and solution finding process. This structure will help develop a new evolutionary algorithm. Furthermore, a new concept of manifold drift for avoiding the guessing process in order to speed up the solution search is also discussed.
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