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This paper presents a novel clustering method this is called extension genetic algorithm (EGA). The new method is a combination of extension theory and genetic algorithm (GA). In the past, we used the extension method in some clustering problems. With the method, we had to rely on experiences to set rules on classical domain and weight, which caused to increase two tedious and complicated steps in clustering processes. In order to improve this defect, the paper uses the EGA to find the best parameter of classical domain. Through the simulations, we prove that this new method can eliminate try and error adjustment of modeling parameters and increase the accuracy of clustering problems. Experimental results from three different examples, including two benchmark data sets and one practical application, verify the effectiveness and applicability of the proposed work. [Copyright &y& Elsevier]

For continuous systems with polytopic uncertainties, the mixed H 2/H ∞ robust output control problem is considered in this paper. The design objective is to seek a static output feedback controller that is able to stabilize the uncertain system and to minimize the mixed H 2/H ∞ norm. To this end, a hybrid algorithm mixed by the technique of Hurwitz testing matrix of family of polynomial, average performance constraint and genetic algorithm (GA) is proposed. Due to the fact that the algorithm is based on GAs, we use a hierarchical structure to merge the conditions of Hurwitz testing matrix technique and average performance constraint into a fitness function which is called Hierarchical Fitness Function Structure (HFFS). By replacing fitness function with HFFS, any GAs can be applied to constitute the algorithm. Also, the proposed approach does not need any assumptions, so it is much more relaxed than that of Lyapunov-based design methods. [Copyright &y& Elsevier]