• Journal of Microbiology and Biotechnology
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• v.4 no.2
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• pp.146-150
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• 1994

Production of poly-$\beta$-hydroxybutyrate (PHB) in fed-batch fermentation was studied. Utilization of carbon for PHB biosynthesis was investigated by using feeding solutions with different ratios of carbon to nitrogen (C/N). It was observed that at a high C/N ratio carbon source was more preferably utilized for PHB accumulation while its consumption for cellular metabolism appeared to be more favored at a low C/N value. A high cell concentration (184 g/l) was achieved when ammonium hydroxide solution was fed to control the pH, which was also utilized as the sole nitrogen source. For the mass production of PHB, two-stage fed-batch operations were carried out where PHB accumulation was observed to be stimulated by switching the ammonium feeding mode to the nitrogen limiting condition. A large amount of PHB (108 g/l) was obtained with cellular content of 80% within 50 hrs of operation.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.2
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• pp.66-73
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• 1997

In many optimal methods for the structural design, the structural analysis is performed with the given design parameters. Then the design sensitivity is calculated based on its structural anaysis results. There-after, the design parameters are changed iteratively. But genetic algorithm is a optimal searching technique which is not depend on design sensitivity. This method uses for many design para- meter groups which are generated by a designer. The generated design parameter groups are become initial population, and then the fitness of the all design parameters are calculated. According to the fitness of each parameter, the design parameters are optimized through the calculation of reproduction process, degradation and interchange, and mutation. Those are the basic operation of the genetic algorithm. The changing process of population is called a generation. The basic calculation process of genetic algorithm is repeatly accepted to every generation. Then the fitness value of the element of a generation becomes maximum. Therefore, the design parameters converge to the optimal. In this study, the optimal design pro- cess of a machine tool structure for static loading is presented to determine the optimal base supporting points and structure thickness using a genetic algorithm.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.289-298
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• 2000

In order to minimize the impact of water hammer In a pipeline, a determination of optimum position of hydraulic structures with best operation of pressure relief valve was explored at the Seobyun pumping station. Method of characteristics is used to simulate a surge impact originating from abrupt stop of pumping operation in a pipeline. Genetic algorithm shows a powerful capability in searching a global solution, especially for a nonlinear problem The application results suggests that the maximum positive pressure can be relaxed by decreasing the opening time of pressure relief valve, meanwhile the maximum negative pressure can be relaxed by increasing the opening time of pressure relief valve. This study shows that the integration of a genetic algorithm with a transient analysis technique such as method of characteristic can improve the design of hydraulic structure in a pipe network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.12C
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• pp.1215-1227
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• 2002

This paper presents a genetic algorithmic approach to shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation that exchanges partial chromosomes (partial-routes) at positionally independent crossing sites and the mutation operation maintain the genetic diversity of the population. The proposed algorithm can cure all the infeasible chromosomes with a simple repair function. Crossover and mutation together provide a search capability that results in improved quality of solution and enhanced rate of convergence. Computer simulations show that the proposed algorithm exhibits a much better quality of solution (route optimality) and a much higher rate of convergence than other algorithms. The results are relatively independent of problem types (network sizes and topologies) for almost all source-destination pairs.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.360-362
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• 2003

In operation of distribution system, DGs(Distributed Generations) are installed as an alternative of extension and establishment of substations, transmission and distribution lines according to increasing power demand. Optimal capacity and allocation of DGs improve power quality and reliability. This paper proposes a method for determining the optimal number, size and allocation of DGs needed to minimize operation cost of distribution system. Capacity of DGs for economic operation of distribution system can be estimated by the load growth and line capacity during operation planning duration. DG allocations are determined to minimize total cost with failure rate and annual reliability cost of each load point using GA(Genetic Algorithm).

• Bulletin of the Korean Chemical Society
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• v.31 no.5
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• pp.1247-1251
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• 2010

We propose an improved solvent contact model to estimate the solvation free energies of amino acids from individual atomic contributions. The modification of the solvation model involves the optimization of three kinds of parameters in the solvation free energy function: atomic fragmental volume, maximum atomic occupancy, and atomic solvation parameters. All of these atomic parameters for 17 atom types are developed by the operation of a standard genetic algorithm in such a way to minimize the difference between experimental and calculated solvation free energies. The present solvation model is able to predict the experimental solvation free energies of amino acids with the squared correlation coefficients of 0.94 and 0.93 for the parameterization with Gaussian and screened Coulomb potential as the envelope functions, respectively. This result indicates that the improved solvent contact model with the newly developed atomic parameters would be a useful tool for the estimation of the molecular solvation free energy of a protein in aqueous solution.

• Industrial Engineering and Management Systems
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• v.11 no.2
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• pp.170-175
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• 2012

In this paper, a procedure using a genetic algorithm (GA) and a heuristic local search (HLS) is proposed for solving facility rearrangement problem (FRP). FRP is a decision problem for stopping/running of facilities and integration of stopped facilities to running facilities to maximize the production capacity of running facilities under the cost constraint. FRP is formulated as an integer programming model for maximizing the total production capacity under the constraint of the total facility operating cost. In the cases of 90 percent of cost constraint and more than 20 facilities, the previous solving method was not effective. To find effective alternatives, this solving procedure using a GA and a HLS is developed. Stopping/running of facilities are searched by GA. The shifting the production operation of stopped facilities into running facilities is searched by HLS, and this local search is executed for one individual in this GA procedure. The effectiveness of the proposed procedure using a GA and HLS is demonstrated by numerical experiment.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.2
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• pp.383-394
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• 2001

The stabilization control of inverted pendulum (IP) system is difficult because of its nonlinearity and structural unstability. In this paper, an Evolving Neural Network Controller (ENNC) without Error Back Propagation (EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC)are compared with the one of conventional optimal controller and the conventional evolving neural network controller (CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Journal of Power System Engineering
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• v.3 no.4
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• pp.63-68
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• 1999

The determination of the optimal machining parameters in metal cutting, such as cutting speed, feed rate, and depth of cut, is an important aspect in an economic manufacturing process. The main objective in general is either to minimize the production cost or to maximize the production rate. Also there are constraints on all the machining operations which put restrictions on the choice of the machining parameters. In this paper as an objective function the production cost is considered with two constraints, surface finish and cutting power. Genetic Algorithm is applied to determine the optimum machining parameters, and the effectiveness of the applied algorithm is demonstrated by means of an example, turning operation.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1113-1120
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• 2004

This paper proposes a novel hybrid genetic algorithm for the feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of the fine-tuning power, and their effectiveness and timing requirement are analyzed and compared. Experimentations performed with various standard datasets revealed that the proposed hybrid GA is superior to a simple GA and sequential search algorithms.