Abstract-Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem.In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the multivariate feature information. Empirical study on commonly data sets of the university of California, Irvine shows that the proposed method outperforms existing methods.

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In this paper we introduce a new software architecture for Autonomic Grid computing networks by military C4ISR (Command, Control, Communications, Computers and Intelligence, Surveillance, & Reconnaissance) architecture. We discuss Self-* property of autonomic grid computing networks in autonomic grid computing networks can lead and manage by C4ISR architecture because we found many held in common properties of autonomic grid computing networks and C4ISR discusses their properties. Finally, we introduce a new software architecture for self-* properties in autonomic grid computing networks as AGC4ISR. Then we introduce some application of AGC4ISR architecture for many critical areas and how can use of it.