• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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• pp.501-514
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• 1999

Multiple U-typed production lines are increasingly accepted in modern manufacturing system for the flexibility to adjust to changes in demand. This paper considers multiple U line balancing with the objective of minimizing cycle time considering of moving time of workforce given the number of workstation. Like the traditional line balancing problem this problem is NP-hard. In this paper, we show how genetic algorithm can be used to solve multiple U line balancing. For this, an encoding and a decoding method suitable to the problem are presented. Proper genetic operators are also employed. Extensive computational experiments are carried out to show the performance of the proposed algorithm. The computational results show that the algorithm is promising in solution quality.

• Annual Conference of KIPS
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• 2004.11a
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• pp.459-462
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• 2004

This paper proposes a genetically inspired adaptive clustering algorithm. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster purity. Chromosome encoding that ensures the correct number of clusters and cluster purity is discussed. The required fitness function is desisted on the basis of modified similarity criteria and genetic operators. These are incorporated into the proposed adaptive clustering algorithm. Experimental results show the efficiency of the clustering algorithm on synthetic data sets and real world data sets.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.912-922
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• 2007

Evaluation of distance teaming systems needs a method that maintains a consistent level of difficulty for each test. In this paper, we propose a new algorithm for test sheet generation based on genetic algorithm. Unlike the existing methods that difficulty of each test item is assigned by tutors, in the proposed method, that can be adjusted by the result of the previous tests and the average difficulty of test sheet can be consistently reserved. We propose the new genetic operators to prevent duplications of test items in a test sheet and apply the adjusted difficulty of each test item. The result of simulation shows that difficulty of the test sheet generated by proposed method can be more regular than the random method and the simulated annealing method.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.64-69
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• 1993

The aim of the present paper is to show the effectiveness of Genetic Algorithm for data classification problems in which the classification criteria are not the Euclidean distance. In particular, in order to improve a search performance of Genetic Algorithm, we introduce a concept of the degree of population diversity, and propose construction of genetic operators and the method of calculation for the fitness of an individual using the degree of population diversity. Then, we investigate their performances through numerical simulations.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.109-114
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• 2012

In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.570-576
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• 2004

It is very difficult to efficiently solve a large-scaled maximal covering problem(MCP) by a genetic algorithm. In this paper, we present new crossover and mutation operators specially designed for genetic algorithms to solve large-scaled MCPs efficiently. We also introduce a novel genetic algorithm employing unexpressed genes. Unexpressed genes are the genes which are not expressed and thus do not affect the evaluation of the individuals. These genes play the role of reserving information susceptible to be lost by the application of genetic operations but is suspected to be potentially useful in later generations. The genetic algorithm employing unexpressed genes enjoys the advantage of being able to maintain diversity of the population and thus can search more efficiently to solve large-scaled MCPs. Experiments with large-scaled real MCP data has shown that our genetic algorithm employing unexpressed genes significantly outperforms tabu search which is one of the popularly used local neighborhood search algorithms for optimization.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.4
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• pp.187-201
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• 1998

In ATM networks with high capacity, the effect of failures on transmission links or nodes can be catastrophic, so that the issue of survivability is of great importance. In this paper. we consider the routing problem for working and backup virtual paths(VPs). To accomplish a higher survivability. routing the two kinds of VPs should be taken into account at the same time because backup VP routing depends on the working VP routing. A coevolutionary algorithm is employed to solve the problem for simultaneously routing of working and backup VPs. To develop an efficient coevolutionary algorithm for the problem. structure of populations, encoding method, neighborhood, and genetic operators are studied in this paper. The results of extensive experiments are reported. The performance comparison of the proposed algorithm with a conventional genetic algorithm and existing heuristics shows that our approach is promising.

• Journal of the Korean Ceramic Society
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• v.48 no.6
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• pp.493-498
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• 2011

Optimal atomic configurations in a nanoparticle were predicted by genetic algorithm. A truncated octahedron with a fixed composition of 1 : 1 was investigated as a model system. A Python code for genetic algorithm linked with a molecular dynamics method was developed. Various operators were implemented to accelerate the optimization of atomic configuration for a given composition and a given morphology of a nanoparticle. The combination of random mix as a crossover operator and total_inversion as a mutation operator showed the most stable structure within the shortest calculation time. Pt-Ag core-shell structure was predicted as the most stable structure for a nanoparticle of approximately 4 nm in diameter. The calculation results in this study led to successful prediction of the atomic configuration of nanoparticle, the size of which is comparable to that of practical nanoparticls for the application to the nanocatalyst.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.123-128
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• 2002

A fuzzy traffic controller with the control rules and the membership functions generated by using genetic algorithm is presented for crossroad management. Conventional fuzzy traffic controllers use control rules and membership functions generated by human operators. However, this approach does not guarantee the optimal solution to design fuzzy control system. Genetic algorithm is a good solution for an optimal problem requiring domain-specific knowledge that is often heuristic. In this paper, we use genetic algorithms to automatically determine the near optimal rules and their membership functions of fuzzy traffic controllers. The effectiveness of our method was shown through simulation of crossroad network.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.9
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• pp.53-62
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• 1998

In this paper, a genetic algorithm-based optimization technique for stereo matching is proposed. Stereo matching is an essential process to recover three-dimensional structure of objects. The proposed two-dimensional chromosomes consist fo disparity values. The cost function of each chromosome is composed of the intensity-difference between two images and smoothness of disparity. The crossover and mutation operators in the two-dimensional chromosomes are described. The operations are affected by the disparities of neighbor pixels. The knowledge-augmented operators are shown to result in rapid convergence and stable result. The genetic algorithm for stereo matching is tested on synthetic and natural images. Experimental results of various images show that the proposed algorithm has good performance even if the images have too dense or sparse feature points. severe noise, and repeating pattern.