• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.5
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• pp.429-436
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• 2016

In this study, the genetic algorithm based optimal structural design method is proposed. The objective functions are to minimize the cost and $CO_2$ emissions, simultaneously. The cost and $CO_2$ emissions are calculated based on the cross-sectional dimensions, length, material strength, and reinforcement ratio of beam and column members. Thus, the cost and $CO_2$ emissions are evaluated by using the amounts of concrete and reinforcement used to construct a building. In this study, the cost and $CO_2$ emissions calculated at the phases of material transportation, construction, and building operation are excluded. The constraint conditions on the strength of beam and column members and the inter-story drift ratio are considered. The linear static analysis by using OpenSees is automatically conducted in the proposed method. The genetic algorithm is employed to solve the formulated problem. The proposed method is validated by applying it to the 4-story reinforced concrete moment frame example.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1415-1421
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• 2016

There are considerable concern on the methods for the efficient utilization of the test-resources as increasing of the number of the tests for functionality and performance verification of weapon systems. Furthermore, with an increase in the complexity of the resource assignment the decision support is required. Test resource allocation is basically the same problems as conventional NP-hard FJSP(Flexible Job Shop Problem), therefore empirical test resource allocation method that has been used in many decades is limited in the time performance. Although research has been conducted applying the genetic algorithm to the FJSP, it is limited in the test resource allocation domain in which more than one machine is necessary for a single operation. In this paper, a variable group genetic algorithm is proposed. The algorithm is expected to improve the test plan efficiency by automating and optimizing the existing manual based allocation. The simulation result shows that the algorithm could be applicable to the test plan.

• Korean Journal of Clinical Laboratory Science
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• v.40 no.1
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• pp.41-47
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• 2008

Colorectal carcinoma is occurred frequently to Korean and so ranked the fourth from various cancers. Due to western dietary life, this cancer has been increased continually. Therefore, the study will be needed to find a candidate gene involved in the development and progression of colorectal carcinoma and to diagnose and treatment helpfully. The striking feature from cancer suppressor genes is known for LOH (loss of heterozygosity), which is the method to find allele genetic loss or mutation of cancer cell. The purpose of this study was designed to find a carcinogenic gene from colon cancer using microsatellite marker on 17th and 18th chromosome from 30 subjects. The LOH was investigated in order of D18S59 57% (17/30), TP53CA 50% (15/30), D18S68 47% (14/30), D18S69 43% (13/30). The genetic mutation depends on loci of colorectal carcinoma was shown higher with 2.44 from colon cancer than with 1.25 from right colorectal carcinoma (p<0.032). The genetic mutation with lymph nodes was investigated higher with 2.69 at mutated group than with 1.14 at non-mutated group (p<0.003). At genetic mutated pattern depends on disease stage, there was higher significant difference at III-IV stage 2.50 than that of I-II stage 1.17, respectively (p=0.015). There was no difference at comparison between histological classification and serological CEA increase. The loss on 18q21 found in this study is highly recurrence loci and was observed 43% for Korean with high recurrence. Therefore, LOH is a very useful tool to detect 18q21 loci in clinical application, prior to the treatment of colorectal carcinoma. After the operation of colorectol carcinoma, the efficient application using LOH at operated part tissue which is designed to protect the recurrence as well as its cure will be needed.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.11-25
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• 2012

We present the method to infer Context-Free Grammars by applying genetic algorithm to the Binary Third-order Recurrent Neural Networks(BTRNN). BTRNN is a multiple-layered architecture of recurrent neural networks, each of which is corresponding to an input symbol, and is combined with external stack. All parameters of BTRNN are represented as binary numbers and each state transition is performed with any stack operation simultaneously. We apply Genetic Algorithm to BTRNN chromosomes and obtain the optimal BTRNN inferring context-free grammar of positive and negative input patterns. This proposed method infers BTRNN, which includes the number of its states equal to or less than those of existing methods of Discrete Recurrent Neural Networks, with less examples and less learning trials. Also BTRNN is superior to the recent method of chromosomes representing grammars at recognition time complexity because of performing deterministic state transitions and stack operations at parsing process. If the number of non-terminals is p, the number of terminals q, the length of an input string k, and the max number of BTRNN states m, the parallel processing time is O(k) and the sequential processing time is O(km).

• KIEE International Transactions on Power Engineering
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• v.5A no.2
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• pp.116-124
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• 2005

This paper presents an application of the parallel Genetic Algorithm-Tabu Search (GA- TS) algorithm, and that is to search for an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration of distribution systems is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to solve the optimal switch position because of its numerous local minima. This paper develops a parallel GA- TS algorithm for the reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10$\%$ of the population to enhance the local searching capabilities. With migration operation, the best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based rapid Ethernet. To demonstrate the usefulness of the proposed method, the developed algorithm was tested and is compared to a distribution system in the reference paper From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution quality, speedup, efficiency, and computation time.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.39-45
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• 2008

This paper considers the multi-objective optimization of a multi-service arrayed waveguide grating-based single-hop WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. This paper presents a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Genetic algorithm based methodology provides the network architecture parameters and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with this methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.943-948
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• 2018

In this paper, we propose a hybrid approach of combining $A^*$ and Genetic algorithm in the path search problem. In $A^*$, the cost from a start node to the intermediate node is optimized in principle but the path from that intermediate node to the goal node is generated and tested based on the cumulated cost and the next node in a priority queue is chosen to be tested. In that process, we adopt the genetic algorithm principle in that the group of nodes to generate the next node from an intermediate node is tested by its fitness function. Top two nodes are selected to use crossover or mutation operation to generate the next generation. If generated nodes are qualified, those nodes are inserted to the priority queue. The proposed method is compared with the original sequential selection and the random selection of the next searching path in $A^*$ algorithm and the result verifies the superiority of the proposed method.

• Microbiology and Biotechnology Letters
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• v.16 no.6
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• pp.450-456
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• 1988

For optimal production of L-tryptophan using a regulatory mutant of Escherichia coli the relationship between product formation and acid production was investigated. Experimental results showed that the production level of L-tryptophan was lowered as the specific acid production rate increased. In order to reduce the amount of acid produced during the fermentation, a controlled fed-batch fermentation was employed. In this fed-batch process, the feed rate of the nutrient feed medium was controlled in relation to the oxygen level in the culture and thus the growth of the cells was regulated in such n way that the oxygen demand of the culture could not exceed the oxygen sup-ply. When E. coli cells were cultivated in a controlled fed-batch mode of tormentor operation, the specific acid production rate was significantly reduced and L-tryptophan production was increased as much as five times that obtained in a conventional fed-batch fermentation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.38
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• pp.43-50
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• 1996

Evolution Programs, a form of Genetic Algorithms transformed from chromosome representation, are applied to the Timetabling of University which is one of the NP-hard problems. At the step of algorithms application, each class is established to be a specific category in feasible solution space. At. the same time, the exiting gene used in chromosome expression of Evolution Programs is modified to satisfy constraints effectively by transformation of gene which has multi-information. The new crossover method for fester operation in the Recombination attempted.. Roulette wheel selection and tournament selection are prepared.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.4
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• pp.657-666
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• 2004

A Design of GA-based SPS controller for power system stabilization was investigated in this paper. The design problem of SPS controller is formulated as an optimization problem using GA. The dynamic characteristic responses are considered to verify the performance of the proposed SPS under various disturbances and operation conditions. The simulation results show that the proposed SPS controller provides most of the damping and improves greatly the voltage profile of the system under two different disturbances.