• Journal of Plant Biology
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• v.38 no.4
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• pp.329-336
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• 1995

Raphanus sativus L. var. hortensis f. raphanistroides (wild radish: Brassicaceae), a herbaceous perennial, occurs only on beaches in East Asia. Genetic diversity and population structure of seven Korean populations were investigated using starch gel electrophoresis. Although the Korean populatins are small, isolated with patchy distribution, the population maintain a moderate level of genetic diversity; the mean percentage fo polymorphic loci was 51.4%, mean number of alleles per locus was 1.84, and mean expected heterozygosity was 0.116. A combination of animal-outcrossing breeding system, wide geographical distribution, restricted ecological distribution, and a propensity for high fecundity may in part be explanatory factors contributing the moderate level of genetic diversity within populations. An overall excess of homozygotes relative to Hardy-Weinberg expetations (mean FISa=0.116) indicates that consanguineous mating occur within wild radish populations, leading to a family structure within a circumscribed area. Although population of wild radish experience a limited gene flow, only 5% of the total genetic variation found in Korean wild radish populations examined is due to differences among populations (mean GST=0.052). This value is considerably lower than the mean values of species with similar life history and ecological characteristics. However, significant differences were found in allele frequencies between populations for all polymorphic loci (P<0.01). It is supposed that directional selection toward genetic uniformity (similar gene frequencies) in a relatively homogenous habitat is thought to be operated among Korean wild radish populations.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.360-367
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• 2002

In this paper, genetic algorithm based adaptive image enhancement filtering scheme is proposed and Implemented on FPGA board. Conventional filtering methods require a priori noise information for image enhancement. In general, if a priori information of noise is not available, heuristic intuition or time consuming recursive calculations are required for image enhancement. Contrary to the conventional filtering methods, the proposed filter system can find optimal combination of filters as well as their sequent order and parameter values adaptively to unknown noise types using structured genetic algorithms. The proposed image enhancement filter system is mainly composed of two blocks. The first block consists of genetic algorithm part and fitness evaluation part. And the second block consists of four types of filters. The first block (genetic algorithms and fitness evaluation blocks) is implemented on host computer using C code, and the second block is implemented on re-configurabe FPGA board. For gray scale control, smoothing and deblurring, four types of filters(median filter, histogram equalization filter, local enhancement filter, and 2D FIR filter) are implemented on FPGA. For evaluation, three types of noises are used and experimental results show that the Proposed scheme can generate optimal set of filters adaptively without a pioi noise information.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.721-726
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• 2020

Genetic algorithm was first introduced to study adaption phenomena occurring in nature based on Darwin's theory of survival of the fittest. It has been used when analytical approach is not possible because of a large number of variables. In this paper, we demonstrated that genetic algorithm could be used to obtain physically optimized experimental values. We programmed a genetic algorithm that uses a few Gaussian functions to find a given function value and the same algorithm was connected to the laser system to obtain laser pulses of 40fs of maximum pulse width and 1mJ of maximum output power. This study shows that genetic algorithm can be applied to laser systems to obtain the optimized laser pulses.

• Computers and Concrete
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• v.6 no.3
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• pp.253-268
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• 2009

This study aims to develop a cost-based high-performance concrete (HPC) mix optimization system based on an integrated approach using artificial neural networks (ANNs) and genetic algorithms (GA). ANNs are used to predict the three main properties of HPC, namely workability, strength and durability, which are used to evaluate fitness and constraint violations in the GA process. Multilayer back-propagation neural networks are trained using the results obtained from experiments and previous research. The correlation between concrete components and its properties is established. GA is employed to arrive at an optimal mix proportion of HPC by minimizing its total cost. A system prototype, called High Performance Concrete Mix-Design System using Genetic Algorithm and Neural Networks (HPCGANN), was developed in MATLAB. The architecture of the proposed system consists of three main parts: 1) User interface; 2) ANNs prediction models software; and 3) GA engine software. The validation of the proposed system is carried out by comparing the results obtained from the system with the trial batches. The results indicate that the proposed system can be used to enable the design of HPC mix which corresponds to its required performance. Furthermore, the proposed system takes into account the influence of the fluctuating unit price of materials in order to achieve the lowest cost of concrete, which cannot be easily obtained by traditional methods or trial-and-error techniques.

• Journal of Korean Association for Spatial Structures
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• v.14 no.4
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• pp.97-104
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• 2014

Recently, a concept of damped outrigger system has been proposed for tall buildings. Structural characteristics and design method of this system were not sufficiently investigated to date. In this study, control performance of damped outrigger system for building structures subjected to seismic excitations has been investigated. And optimal design method of damped outrigger system has been proposed using multi-objective genetic algorithm. To this end, a simplified numerical model of damped outrigger system has been developed. State-space equation formulation proposed in previous research was used to make a numerical model. Multi-objective genetic algorithms has been employed for optimal design of the stiffness and damping parameters of the outrigger damper. Based on numerical analyses, it has been shown that the damped outrigger system control dynamic responses of the tall buildings subjected to earthquake excitations in comparison with a traditional outrigger system.

• Smart Structures and Systems
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• v.15 no.3
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• pp.897-911
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• 2015

This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.297-300
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• 1997

This paper uses genetic algorithm (GA) for optimal control. GA can find optimal control profile, but the profile may be oscillating feature. To make profile smooth, fuzzy genetic algorithm (FGA) is proposed. GA with fuzzy logic techniques for optimal control can make optimal control profile smooth. We describe the Fuzzy Genetic Algorithm that uses a fuzzy knowledge based system to control GA search. Result from the simulation example shows that GA can find optimal control profile and FGA makes a performance improvement over a simple GA.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.127-135
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• 1999

Multivarialbe Boiler-Turbine H_\infty Control System Genetic Algorithm Weighting Functions $W_1$(s), $W_2$(s), and design parameter $\gamma$ that are given by Glover-Doyle algorithm, to optimally follow the output of reference model. The first method to do this is that the gains of weighting functions $W_1$(s), $W_2$(s), and design parameter are optimized simultaneously by genetic algorithm with the tournament method that can search more diversely, in the search domain which guarantees the robust stability of system. And the second method is that not only by genetic algorithm with the roulette-wheel method that can search more fast, in that search domain. The boiler-turbine H_\infty control system designed by theabove second method has not only the robust stability to a modeling error but also the the better command tracking preformance than those of the H_\infty control system designed by trial-and-error method and the above first method. Also, this boiler-turbine H_\infty control system has the better performance than that of the LQG/LTR contro lsystem. The effectiveness of this boiler-turbineH_\infty control system is verified by computer simulation.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.1
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• pp.124-131
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• 2005

This paper suggests LOS(Line-Of-Sight) guidance system of a surface vessel in straight-line navigation under ocean currents An LOS vector from the vessel to a point on the path between two way-points is decided and a heading angle is calculated to converge to follow the desired path based on the LOS vector This guidance system is called LOS guidance system. The suggested LOS guidance law has parameters to be properly chosen according to navigational environment. Parameters of LOS guidance system are optimized to reduce propulsive energy and/or position error between desired Position and present position of a ship using genetic algorithm which is a strong optimization algorithm with adaptational random search The effectiveness of the suggested LOS guidance system is assured through computer simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.255-258
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• 2001

This paper is implementation of cellular automata neural network system which is a living creatures' brain using evolving hardware concept. Cellular automata neural network system is based on the development and the evolution, in other words, it is modeled on the ontogeny and phylogeny of natural living things. The proposed system developes each cell's state in neural network by CA. And it regards code of CA rule as individual of genetic algorithm, and evolved by genetic algorithm. In this paper we implement this system using evolving hardware concept Evolving hardware is reconfigurable hardware whose configuration is under the control of an evolutionary algorithm. We design genetic algorithm process for evolutionary algorithm and cells in cellular automata neural network for the construction of reconfigurable system. The effectiveness of the proposed system is verified by applying it to time-series prediction.