• 제어로봇시스템학회논문지
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• 제6권11호
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• pp.995-1002
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• 2000

In this paper, fuzzy logic controllers(FLC) are designed for control of flight. For tuning FLC, we used adaptive evolutionary algorithms(AEA) which uses a genetic algorithm(GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations. We used AEA to search for optimal settings of the membership functions shape and gains of the inputs and outputs of FLC. Finally, the proposed controller is applied to the high-angle-of-attack flight system for a supermaneuverable version of the f-18 aircraft and compares with other methods.

• 대한전기학회논문지:전력기술부문A
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• 제49권5호
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• pp.205-211
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• 2000

This paper presents an optimal design method for fuzzy logic controller (FLC) of HVDC using an Adaptive Evolutionary Algorithm(AEA). We have proposed the AEA which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary algorithms. The AEA is used for tuning fuzzy membership functions and scaling constants. Simulation results show that disturbances are well damped and the dynamic performances of FLC have better responses than those of PD controller when AC system load changes suddenly.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1177-1179
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• 1999

This paper presents a design of artificial neural network power system stabilizer(ANNPSS) using adaptive evolutionary algorithm(AEA). We have proposed an adaptive evolutionary algorithm which uses both a genetic algorithm(GA) and an evolution strategy(ES), useing the merits of two different evolutionary computations. ANNPSS shows better control performances than conventional power system stabilizer(CPSS) in three-phase fault with heavy load which is used when tuning ANNPSS. To show the robustness of the proposed ANNPSS, it is applied to damp the low frequency oscillation caused by disturbances such as three-phase fault with normal and light load. the proposed ANNPSS shows better robustness than CPSS.

• Journal of Mechanical Science and Technology
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• 제17권2호
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• pp.187-196
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• 2003

In this paper, a new methodology of evolutionary computations - An Adaptive Evolutionary Algorithm (AEA) is proposed. AEA uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations : global search capability of GA and local search capability of ES. In the reproduction procedure, the proportions of the population by GA and ES are adaptively modulated according to the fitness. AEA is used to. designing fuzzy logic controller (FLC) for a high-angle-of-attack flight system for a super-maneuverable version of F-18 aircraft. AEA is used to determine the membership functions and scaling factors of an FLC. The computer simulation results show that the FLC has met both robustness and performance requirements.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 A
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• pp.160-162
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• 1999

This paper presents an optimal design method for fuzzy controllers of HVDC system using adaptive evolutionary algorithm(AEA). We have proposed an adaptive evolutionary algorithm which uses a genetic algorithm and an evolution strategy in an adaptive manner in order to take merits of two different evolutionary computations. AEA is used for tuning fuzzy membership functions, scaling constants and PD gains. The simulation results show that the disturbances are well damped by both controllers and the dynamic performances of fuzzy controllers have better responses than those of PD controllers when mechanical torque changes suddenly.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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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.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.730-734
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• 2001

In this study in order to confirm the algorithms that are suggested from paper (I) as the experimental result, as the applied results of the hydraulic servo system are very strong a non-linearity of the fluid in the computer simulation, the real-time adaptive learning control algorithms is validated. The evolutionary strategy has characteristics that are automatically. adjusted in search regions with natural competition among many individuals. The error that is generated from the dynamic system is applied to the mutation equation. Competitive individuals are reduced with automatic adjustments of the search region in accord with the error. In this paper, the individual parents and offspring can be reduced in order to apply evolutionary algorithms in real-time as the description of the paper (I). The possibility of a new approaching algorithm that is suggested from the computer simulation of the paper (I) would be proved as the verification of a real-time test and the consideration its influence from the actual experiment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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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.

• 한국미생물·생명공학회지
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• 제48권4호
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• pp.423-428
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• 2020

In vitro evolution is a powerful technique for the engineering of yeast strains to study cellular mechanisms associated with evolutionary adaptation; strains with desirable traits for industrial processes can also be generated. There are two distinct approaches to generate evolved strains in vitro: the sequential transfer of cells in the stationary phase into fresh medium or the continuous growth of cells in a chemostat bioreactor via the constant supply of fresh medium. In culture, evolutionary forces drive diverse adaptive mechanisms within the cell to overcome environmental or intracellular stressors. Especially, this engineering strategy has expanded to the field of human cell lines; the understanding of such adaptive mechanisms provides promising targets for the treatment of human genetic diseases and cancer. Therefore, this technology has the potential to generate numerous industrial, medical, and academic applications.