• Title/Summary/Keyword: evolutionary computation

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Evolutionary Topic Maps (진화연산을 통해 만들어지는 토픽맵)

  • Kim, Ju-Ho;Hong, Won-Wook;McKay, Robert Ian
    • 한국HCI학회:학술대회논문집
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    • 2009.02a
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    • pp.685-689
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    • 2009
  • Evolutionary Computation is not only widely used in optimization and machine learning, but also being applied in creating novel structures and entities. This paper proposes evolutionary topic maps that can suggest new and creative knowledge not easily producible by humans. Interactive evolutionary computation method is applied into topic maps in order to accept human evaluation on feasibility of intermediate topic maps. Evolutionary topic maps are creativity support tools, helping users to encounter new and creative knowledge. Further work can greatly improve the system by providing more operations, preventing over-convergence, and overcoming user fatigue problem by providing more intuitive user interface, better visualization, and interpolation mechanisms.

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Comparison of Evolutionary Computation for Power Flow Control in Power Systems (전력계통의 전력조류제어를 위한 진화연산의 비교)

  • Lee, Sang-Keun
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.54 no.2
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    • pp.61-66
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    • 2005
  • This paper presents an unified method which solves real and reactive power dispatch problems for the economic operation of power systems using evolutionary computation such as genetic algorithms(GA), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most of these approaches have the common defect of being caught to a local minimum solution. The proposed methods, applied to the IEEE 30-bus system, were run for 10 other exogenous parameters and composed of P-optimization module and Q-optimization module. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

Implementation of Fuzzy Controller of DC Motor Using Evolutionary Computation (진화 연산을 이용한 DC 모터 퍼지 제어기 구현)

  • Hwang, G.H.;Kim, H.S.;Mun, K.J.;Lee, H.S.;Park, J.H.;Hwang, C.H.
    • Proceedings of the KIEE Conference
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    • 1995.11a
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    • pp.189-191
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    • 1995
  • This paper proposes a design of self-tuning fuzzy controller based on evolutionary computation. Optimal membership functions are found by using evolutionary computation. Genetic algorithms and evolution strategy are used for tuning of fuzzy membership function. An arbitrarily speed trajectory is selected to show the performance of the proposed methods. Experiment results show the good performance in the DC motor control system with the self-tuning fuzzy controller based on evolutionary computation.

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A DC Motor Speed Control using Fuzzy System and Evolutionary Computation (퍼지 시스템과 진화연산을 이용한 DC 모터 속도제어)

  • Hwang, K.H.;Mun, K.J.;Lee, H.S.;Kim, H.S.;Park, J.H.
    • Proceedings of the KIEE Conference
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    • 1995.07b
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    • pp.652-654
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    • 1995
  • This paper proposes a design of self-tuning fuzzy controller based on evolutionary computation. Optimal membership functions are round by using evolutionary computation. Genetic algorithms and evolution strategy are used for tuning of fuzzy membership function. A arbitrarily speed trajectories is selected to show the performance of the proposed methods. Simulation results show the good performance in the DC motor control system with the self-tuning fuzzy controller based on evolutionary computation.

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An Optimal Real and Reactive Power dispatch using Evolutionary Computation (진화연산을 이용한 유효 및 무효전력 최적배분)

  • You, Seok-Ku;Park, Chang-Joo;Kim, Kyu-Ho
    • Proceedings of the KIEE Conference
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    • 1996.11a
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    • pp.166-168
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    • 1996
  • This paper presents an power system optimization method which solves real and reactive power dispatch problems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods, applied to the IEEE 30-bus system, were run for 12 other exogenous parameters. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

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A Fuzzy Logic Controller for Speed Control of a DC Series Motor Using an Adaptive Evolutionary Computation

  • Hwang, Gi-Hyun;Hwang, Hyun-Joon;Kim, Dong-Wan;Park, June-Ho
    • Transactions on Control, Automation and Systems Engineering
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    • v.2 no.1
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    • pp.13-18
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    • 2000
  • In this paper, an Adaptive Evolutionary Computation(AEC) is proposed. AEC uses a genetic algorithm(GA) and an evolution strategy (ES) in an adaptive manner is order to take merits of two different evolutionary computations: global search capability of GA and local search capability of ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. AEC is used to design the membership functions and the scaling factors of fuzzy logic controller (FLC). To evaluate the performances of the proposed FLC, we make an experiment on FLC for the speed control of an actual DC series motor system with nonlinear characteristics. Experimental results show that the proposed controller has better performance than that of PD controller.

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An Optimization Method using Evolutionary Computation in Large Scale Power Systems (진화연산을 이용한 대규모 전력계통의 최적화 방안)

  • You, Seok-Ku;Park, Chang-Joo;Kim, Kyu-Ho;Lee, Jae-Gyu
    • Proceedings of the KIEE Conference
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    • 1996.07b
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    • pp.714-716
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    • 1996
  • This paper presents an optimization method for optimal reactive power dispatch which minimizes real power loss and improves voltage profile of power systems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP). and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods were applied to the IEEE 30-bus system. Each simulation result, compared with that obtained by using a conventional gradient-based optimization method, Sequential Quadratic Programming (SQP), shows the possibility of applications of evolutionary computation to large scale power systems.

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The Real-time Self-tuning Learning Control based on Evolutionary Computation (진화 연산을 이용한 실시간 자기동조 학습제어)

  • Chang, Sung-Quk;Lee, Jin-Kul
    • Proceedings of the KSME Conference
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    • 2001.06b
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    • pp.105-109
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    • 2001
  • This paper discuss the real-time self-tuning learning control based on evolutionary computation, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes.

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Model development in freshwater ecology with a case study using evolutionary computation

  • Kim, Dong-Kyun;Jeong, Kwang-Seuk;McKay, Robert Ian (Bob);Chon, Tae-Soo;Kim, Hyun-Woo;Joo, Gea-Jae
    • Journal of Ecology and Environment
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    • v.33 no.4
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    • pp.275-288
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    • 2010
  • Ecological modeling faces some unique problems in dealing with complex environment-organism relationships, making it one of the toughest domains that might be encountered by a modeler. Newer technologies and ecosystem modeling paradigms have recently been proposed, all as part of a broader effort to reduce the uncertainty in models arising from qualitative and quantitative imperfections in the ecological data. In this paper, evolutionary computation modeling approaches are introduced and proposed as useful modeling tools for ecosystems. The results of our case study support the applicability of an algal predictive model constructed via genetic programming. In conclusion, we propose that evolutionary computation may constitute a powerful tool for the modeling of highly complex objects, such as river ecosystems.

A Hybrid Method for Improvement of Evolutionary Computation (진화 연산의 성능 개선을 위한 하이브리드 방법)

  • Chung, Jin-Ki;Oh, Se-Young
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.4
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    • pp.317-322
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    • 2002
  • The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.