• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.376-379
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• 2003

DNA computing based DNA sequence Is operated through the biology experiment. Biology experiment used as operator causes illegal reactions through shifted hybridization, mismatched hybridization, undesired hybridization of the DNA sequence. So, it is essential to design DNA sequence to minimize the potential errors. This paper proposes method of the DNA sequence generation based evolutionary operation processor. Genetic algorithm was used for evolutionary operation and extra hardware, namely genetic algorithm processor was implemented for solving repeated evolutionary process that causes much computation time. To show efficiency of the Proposed processor, excellent result is confirmed by comparing between fitness of the DNA sequence formed randomly and DNA sequence formed by genetic algorithm processor. Proposed genetic algorithm processor can reduce the time and expense for preparing DNA sequence that is essential in DNA computing. Also it can apply design of the oligomer for development of the DNA chip or oligo chip.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2827-2829
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• 1999

Genetic algorithms were described as a method of solving large-scaled optimization problems with complex constraints. It has overcome their slowness, a major drawback of genetic algorithms using hardware implementation of genetic algorithm processor (GAP). In this study, we proposed GAP effectively connecting the goodness of survival-based GA, steady-state GA, tournament selection. Using Pipeline Parallel processing, handshaking protocol effectively, the proposed GAP exhibits 50% speed-up over survival-based GA which runs one million crossovers per second(1MHz). It will be used for high speed processing such of central processor of EHW, robot control and many optimization problem.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.120.5-120
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• 2002

1. Introduction 2. Property of FPGA 3. Evolvable Hardware (EHW) 4. Genetic Algorithm Processor (GAP) 5. State Machine 6. Conclusion

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.221-221
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• 2004

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.379-382
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• 2002

GA(Genetic Algorithm)는 자연계 진화를 모방한 계산 알고리즘으로서 단순하고 응용이 쉽기 때문에 여러 분야에 사용되고 있다. 하지만 GA의 단점은 일반적인 소프트웨어로 동작시켰을 때는 실행속도가 느리다는 것이다. 특히 chromosome이 길 경우 연속적인 교차, 돌연변이를 수행해야한다. GA Processor(GAP)는 GA를 수행하기위한 전용 Processor로서 GA의 동작을 빨리 수행할 수 있게 한다. 본 논문에서는 pipeline 구조의 GAP를 설계하여 GA를 수행함에 있어 소프트웨어와 하드웨어의 성능을 비교한다.

• 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.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.157-160
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• 2001

This paper has been studied a Adaptive Mutation rate Genetic Algorithm Processor. Genetic Algorithm(GA) has some control parameters such as the probability of bit mutation or the probability of crossover. These value give a priori by the designer There exists a wide variety of values for for control parameters and it is difficult to find the best choice of these values in order to optimize the behavior of a particular GA. We proposed a Adaptive mutation rate GA within a steady-state genetic algorithm in order to provide a self-adapting mutation mechanism. In this paper, the proposed a adaptive mutation rate GAP is implemented on the FPGA board with a APEX EP20K600EBC652-3 devices. The proposed a adaptive mutation rate GAP increased the speed of finding optimal solution by about 10%, and increased probability of finding the optimal solution more than the conventional GAP

• The KIPS Transactions:PartA
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• v.10A no.1
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• pp.49-58
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• 2003

In a sender-initiated load balancing algorithm, a sender (overloaded processor) continues to send unnecessary request messages for load transfer until a receiver (underloaded processor) is found while the system load is heavy. Therefore, it yields many problems such as low cpu utilization and system throughput because of inefficient inter-processor communications until the sender receives an accept message from the receiver in this environment. This paper presents an approach based on genetic algorithm (GA) for dynamic load balancing in heterogeneous distributed systems. In this scheme the processors to which the requests are sent off are determined by the proposed GA to decrease unnecessary request messages.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.93-101
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• 2004

In a receiver-initiated load redistribution algorithm, receiver(underloaded processor) continues to send unnecessary request messages for load transfer until a sender(overloaded processor) is found while the system load is light, Therefore, it yields many problems such as low cpu utilization and system throughput because of inefficient inter-processor communications until the receiver receives an accept message from the sender in this environment. This paper presents an approach based on genetic algorithm(GA) for dynamic load redistribution in heterogeneous distributed systems. In this scheme the processors to which the requests are sent off are determined by the proposed GA to decrease unnecessary request messages. The performance of proposed algorithm shows better than that of the conventional algorithm through various experiments.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.29-32
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• 2001

In this paper, we applied the high-performance PGAP(Parallel Genetic Algorithm Processor) to recognizing rotated pattern. In order to perform this research efficiently, we used Multi-PGAP system consisted of four PGAP. In addition, we used mental rotation based on the rotated pattern recognition mechanism of human to reduce the number of operation. Also, we experimented with distinguishing specific pattern from similar coin patterns and determine rotated angle between patterns. The result showed that the development of future artificial recognition system is feasible by employing high performance PGAPS.