• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.69-78
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• 2006

In the field of the bioinformatics, it plays an important role in predicting functional information or structure information to search similar sequence in biological DB. Biolrgical sequences have been increased dramatically since Human Genome Project. At this point, because the searching speed for the similar sequence is highly regarded as the important factor for predicting function or structure, the SMP(Sysmmetric Multi-Processors) computer or cluster is being used in order to improve the performance of searching time. As the method to improve the searching time of BLAST(Basic Local Alighment Search Tool) being used for the similarity sequence search, We suggest the nBLAST algorithm performing on the cluster environment in this paper. As the nBLAST uses the MPI(Message Passing Interface), the parallel library without modifying the existing BLAST source code, to distribute the query to each node and make it performed in parallel, it is possible to easily make BLAST parallel without complicated procedures such as the configuration. In addition, with the experiment performing the nBLAST in the 28 nodes of LINUX cluster, the enhanced performance according to the increase in the number of the nodes has been confirmed.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.44-59
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• 2006

This paper describes the efficient implementation of DNA sequence generate system with genetic algorithm for reducing computation time of NACST. The proposed processor is based on genetic algerian with fitness functions which would suit the point of reference for generated sequences. In order to implement efficient hardware structure, we used the pipelined structure. In addition our design was applied the parallelism to achieve even better simulation time than the sequence generator system which is designed on software. In this paper, our hardware is implemented on the FPGA board with xc2v6000 devices. Through experiment, the proposed hardware achieves 467 times speed-up over software on a PC and sequence generate performance of hardware is same with software.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.617-626
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• 2014

Lately, losses and damage from natural disasters have been increasing. Researchers across various fields in Korea are trying to come up with a response plan, but research for evacuation plans is still far from satisfactory. Hence this paper proposes a model that could find an optimized evacuation route for when disasters occur over wide areas. Development of the model used methods including the Dijkstra shortest path algorithm, feasible path method, genetic algorithm, and pareto efficiency. Computations used parallel computing (SPMD) for high performance. In addition, the developed model is applied to a virtual network to check the validity. Finally the adaptability of the model is verified on a real network by computating for Gumi 1stNational Industrial Complex. Computation results proved that this model is valid and applicable by comparison of the fitness values for before optimization and after optimization. This research can contribute to routing for responder vehicles as well as planning for evacuation by objective when disasters occur.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.277-286
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• 2002

Genetic programming (GP) has been combined with quantum mechanical perturbation theory to make a new algorithm to construct mathematical models and perform predictions for chaotic time series from real world. Procedural similarities between time series modeling and perturbation theory to solve quantum mechanical wave equations are discussed, and the exemplary GP approach for implementing them is proposed. The approach is based on multiple populations and uses orthogonal functions for GP function set. GP is applied to original time series to get the first mathematical model. Numerical values of the model are subtracted from the original time series data to form a residual time series which is again subject to GP modeling procedure. The process is repeated until predetermined terminating conditions are met. The algorithm has been successfully applied to construct highly effective mathematical models for many real world chaotic time series. Comparisons with other methodologies and topics for further study are also introduced.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1950-1951
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• 2011

본 논문에서는 요구되는 성능을 만족시키는 최적 Fuzzy PI 제어의 정압제어로의 효율적인 적용 및 성능 향상을 위하여 유전자 알고리즘(GA: Genetic Algorithm)을 이용한 제어 설계 방법을 제시 한다. PID제어기는 이해가 쉽고 구조가 간단하여, 실제 구현이 용이하여 공정 산업분야에서 가장 널리 사용되고 있는 제어기 이다. 따라서 단일 입 출력 선형 시스템 에서는 우수한 성능을 보이나 동적 시스템, 고차 시스템 및 수학적 모델 선정이 어려운 시스템에서는 비효율 적이다. 반면, Fuzzy 제어기는 인간의 지식과 경험을 이용한 지적 제어방식으로 IF-THEN형식의 규칙으로부터 제어 입력을 결정하는 병렬형 제어기이다. 이는 과도상태에서 큰 오버슈트 없이 설정치에 도달하게 하는 속응성과 강인성이 좋은 제어기법으로 비선형성이 강하고 불확실하며 복잡한 시스템을 쉽게 제어 할 수 있다는 장점을 지닌다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.166-169
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• 1997

For parallel processing, the compiler partitions a loaded program into a set of tasks and makes a schedule for the tasks that will minimize parallel processing time for the loaded program. Building an optimal schedule for a given set of partitioned tasks of a program has known to be NP-complete. In this paper we introduce a GA(Genetic Algorithm)-based scheduling method in which a chromosome consists of two parts of a string which decide the number and order of tasks on each processor. An additional computation is used for feasibility constraint in the chromosome. By granularity theory, a partitioned program is categorized into coarse-grain or fine-grain types. There exist good heuristic algorithms for coarse-grain type partitioning. We suggested another GA adaptive to the coarse-grain type partitioning. The infeasibility of chromosome is overcome by the encoding and operators. The number of processors are decided while the GA find the minimum parallel processing time.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2097-2098
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• 2006

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). HFCGA is a kind of multi-populations of Parallel Genetic Algorithms(PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.241-243
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• 2002

DNA 컴퓨팅은 생체 분자들이 갖는 막대한 병렬성을 정보 처리 기술에 적용한 기술이다. Adleman의 DNA 컴퓨팅은 랜덤한 고정길이의 형태로 문제를 표현하기 때문에 해를 찾지 못하거나 시간이 많이 걸리는 단점을 갖고 있다. 본 논문은 DNA 컴퓨팅에 DNA 코딩 방법을 적용하여 DNA 서열을 효율적으로 표현하고 반응횟수 만큼 합성과 분리 과정을 거쳐 최적의 코드를 생성하는 ACO(Algorithm for Code Optimization)를 제안한다. DNA 코딩 방법은 변형된 유전자 알고리즘으로 DNA 기능을 유지하며, 서열의 길이를 줄일 수 있으므로 최적의 서열을 생성할 수 있는 특징을 갖는다. ACO를 NP-complete 문제 중 Hamiltonian path problem에 적용하여 실험한 결과, Adleman의 DNA 컴퓨팅 보다 초기 문제 표현에서 높은 적합도 값을 갖는 서열을 생성했으며, 경로의 변화에도 능동적으로 대처하여 최적의 결과를 빠르게 탐색할 수 있었다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.40-47
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• 2000

One of the drawbacks of GA-based structural optimization is that the fitness evaluation of a population of hundreds of individuals requiring hundreds of structural analyses at each CA generation is computational too expensive. Therefore, a parallel genetic algorithm is developed for structural optimization on a cluster of personal computers in this paper. Based on the parallel genetic algorithm, a population at every generation is partitioned into a number of sub-populations equal to the number of slave computers. Parallelism is exploited at sub-population level by allocationg each sub-population to a slave computer. Thus, fitness of a population at each generation can be concurrently evaluated on a cluster of personal computers. For implementation of the algorithm a virtual distributed computing system in a collection of personal computers connected via a 100 Mb/s Ethernet LAN. The algorithm is applied to the minimum weight design of a steel structure. The results show that the computational time requied for serial GA-based structural optimization process is drastically reduced.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.48-55
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• 2008

In this paper, we propose a hybrid fuzzy controller for high performance. Hybrid fuzzy controller are combined Fuzzy and PID controller. In tuning the controller, the parameters of PID and the factors fuzzy controllers were obtained from the model identification and by using genetic algorithms, respectively. Simulation examples demonstrated a better performance of the proposed controller than conventional ones.