• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.265-276
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• 2004

Genetic Algorithm(GA), that is shown stable performance to find an optimal solution, has been used as a method of solving large-scaled optimization problems with complex constraints in various applications. Since it takes so much time to execute a long computation process for iterative evolution and adaptation. In this paper, a hardware-based adaptive GA was proposed to reduce the serious computation time of the evolutionary process and to improve the accuracy of convergence to optimal solution. The proposed GA, based on steady-state model among continuos generation model, performs an adaptive mutation process with consideration of the evolution flow and the population diversity. The drawback of the GA, premature convergence, was solved by the proposed adaptation. The Performance improvement of convergence accuracy for some kinds of problem and condition reached to 5-100% with equivalent convergence speed to high-speed algorithm. The proposed adaptive GAP(Genetic Algorithm Processor) was implemented on FPGA device Xilinx XCV2000E of EHW board for face recognition.

• Journal of Internet Computing and Services
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• v.1 no.2
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• pp.69-88
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• 2000

One of the most important factors on the performance of the distributed systems is the effective distribution of the software components, There have been a lot of researches on partitioning and allocating the task-based system, while the studies on the allocating the objects of the object-oriented system into the distributed object environments are very little relatively. In this paper. we defines the graph model for partitioning the existing C++ application and allocating the C++ objects into CORBA-base distributed system, In addition, we propose a distributed object allocation algorithm based on this graph model. The performance of distributed systems is determined by the concurrency between objects, the load balance among the processors and the communication cost on the networks. To search for the solutions optimizing the above three factors simultaneously, the object allocation algorithm of this paper is based on the Niched Pareto Genetic Algorithm (NPGA). We performed the experiment on the typical C++ application and CORBA system to prove the effectiveness of our graph model and our object allocation algorithm.