dc.description.abstract | Evolutionary algorithms have been widely used in many fields, such as high energy physics analysis, weather forecasting, genetic analysis, biological research, financial or business information analysis. Although these methods can provide some satisfactory solutions, they have been proved to be time-consuming methods. Therefore, the computational efficiency needs to be taken into account. Although the cloud technique can significantly improve the computing performance, it also increase the complexity of problems, such as more dimensions or numbers of the datasets. In addition, based on No Free Lunch theory (NFL), all evolutionary algorithms must pay the same price for solving problems. In other words, to get a better solution, it must pays more costs, such as more operations, process change, time or individuals.
Therefore, this thesis aims to provide a framework based on the idea which is according to the Divide and Conquer (D&C) principle, and this framework can be applied to all evolutionary algorithms to overcome the large search space problem that can affect the computational efficiency (such as: a huge search space and premature convergence, data screening, black box, etc.). In addition, it also can assist the evolutionary algorithms in recording the sensitive data samples in each dimension during the searching process, which helps users to fully understand the problem.
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