• The Journal of the Korea Contents Association
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• v.7 no.11
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• pp.94-101
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• 2007

We introduce the way to detect the mutation worm. We implemented the program that can generate signature using LCSeq(Longest Common Subsequence) technique in Suffix Tree studied as pattern recognition algorithm. We also showed the process to detect the mutation of CodeRed worm and Nimda worm and evaluated signatures generated by snort and LCSeq.

• IE interfaces
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• v.14 no.3
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• pp.227-235
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• 2001

There is a rapidly growing demand for wireless telecommunication. However, the number of usable channel is very limited. Therefore, the problem of channel assignment becomes more and more important to use channels as efficiently as possible. The objective of this paper is to develop an evolution program (EP) to find an efficient dynamic channel assignment method for minimum interference among the channels within reasonable time. The series of specific channel number is used as a representation of chromosome. The only changed chromosomes by crossover and mutation are evaluated in each generation to save computation time and memory for the progress of improved EP. We can easily differentiate the fitness value of each chromosome using proposed evaluation function. We also control the weighting factor of the mutation rate and the used number of elitist chromosomes for the speed of convergence to the optimal solution.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.672-680
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• 1999

This paper presents an evolution program for optimal expansion problem with backlogs in demand which is to determine when and how much capacity should be added under varying types of demand and cost. To overcome premature convergence and stalling, we employ an exponential-fitness scaling scheme. To improve the chromosomes, we introduce hetero-dimensional mutation which generates a new dimension and produces a feasible solution, and homo-dimensional mutation which mutates the chromosomes in the negative of gradient direction. Finally, a numerical example is discussed.

• Journal of Information Processing Systems
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• v.7 no.2
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• pp.363-384
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• 2011

As the complexity of software is increasing, generating an effective test data has become a necessity. This necessity has increased the demand for techniques that can generate test data effectively. This paper proposes a test data generation technique based on adequacy based testing criteria. Adequacy based testing criteria uses the concept of mutation analysis to check the adequacy of test data. In general, mutation analysis is applied after the test data is generated. But, in this work, we propose a technique that applies mutation analysis at the time of test data generation only, rather than applying it after the test data has been generated. This saves significant amount of time (required to generate adequate test cases) as compared to the latter case as the total time in the latter case is the sum of the time to generate test data and the time to apply mutation analysis to the generated test data. We also use genetic algorithms that explore the complete domain of the program to provide near-global optimum solution. In this paper, we first define and explain the proposed technique. Then we validate the proposed technique using ten real time programs. The proposed technique is compared with path testing technique (that use reliability based testing criteria) for these ten programs. The results show that the adequacy based proposed technique is better than the reliability based path testing technique and there is a significant reduce in number of generated test cases and time taken to generate test cases.

• Bulletin of the Korean Chemical Society
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• v.31 no.7
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• pp.2077-2080
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• 2010

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.81-86
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• 2001

one key goal of software testing is to generate a 'good' test data set, which is consideres as the most difficult and time-consuming task. This paper discusses how genetic algorithns can be used for automatic generation of test data set for software testing. We employ mutation testing to show the effectiveness of genetic algorithms (GAs) in automatic test data generation. The approach presented in this paper is different from other in that test generation process requireas no lnowledge of implementation details of a program under test. In addition, we have conducted some experiments and compared our approach with random testing which is also regarded as a black-box test generation technique to show its effectiveness.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3187-3190
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• 2013

Background: Epidermal growth factor receptor (EGFR) is a transmembrane receptor which contributes to many processes involved in cell survival, proliferation and inhibits apoptosis, that may lead to cancer development. Gastric cancer is one of the most common diseases of digestive system that has low 5-year-survival. The aim of this research was to determine the significance of EGFR tyrosine kinase domain gene polymorphisms in gastric cancer in Iran. Materials and Methods: In the present study, 83 patients with gastric cancer and 40 normal subjects were investigated for EGFR gene polymorphisms in exons 18-21 by PCR-SSCP. Then, DNA sequencing was conducted for different mobility shift bands. Finally the data were statistically analyzed using the chi-2 test and the SPSSver.16 program. Results: Exon 18 of EGFR gene showed three different bands in SSCP pattern and DNA sequencing displayed one mutation. SSCP pattern of Exons 19 and 21 did not show different migration bands. Exon 20 of EGFR gene revealed multiple migrate bands in SSCP pattern. DNA sequencing displayed 2 mutations in this exon: one mutation was caused amino acid change and another mutation was silent. Conclusion: It may be that EGFR tyrosine kinase gene polymorphisms differ between populations and screening could be useful in gastric cancer patients who might benefit from tyrosine kinase inhibitor therapy.

• Journal of Korean Association for Spatial Structures
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• v.5 no.3 s.17
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• pp.117-122
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• 2005

This paper investigated the optimum design of truss structures based on Genetic Algorithms (GA's). With GA's characteristic of running side by side, the overall optimization and feasible operation, the optimum design model of truss structures was established. Elite models were used to assure that the best units of the previous generation had access to the evolution of current generation. Using of non-uniformity mutation brought the obvious mutation at earlier stage and stable mutation in the later stage; this benefited the convergence of units to the best result. In addition, to avoid GA's drawback of converging to local optimization easily, by the limit value of each variable was changed respectively and the genetic operation was performed two times, so the program could work more efficiently and obtained more precise results. Finally, by simulating evolution process of nature biology of a kind self-organize, self-organize, artificial intelligence, this paper established continuous structural optimization model for ten bars cantilever truss, and obtained satisfactory result of optimum design. This paper further explained that structural optimization is practicable with GA's, and provided the theoretic basis for the GA's optimum design of structural engineering.

• Genomics & Informatics
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• v.12 no.4
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• pp.289-292
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• 2014

Next-generation sequencing (NGS) is widely used to identify the causative mutations underlying diverse human diseases, including cancers, which can be useful for discovering the diagnostic and therapeutic targets. Currently, a number of single-nucleotide variant (SNV)-calling algorithms are available; however, there is no tool for visualizing the recurrent and phenotype-specific mutations for general researchers. In this study, in order to support defining the recurrent mutations or phenotype-specific mutations from NGS data of a group of cancers with diverse phenotypes, we aimed to develop a user-friendly tool, named mutation arranger for defining phenotype-related SNV (MAP). MAP is a user-friendly program with multiple functions that supports the determination of recurrent or phenotype-specific mutations and provides graphic illustration images to the users. Its operation environment, the Microsoft Windows environment, enables more researchers who cannot operate Linux to define clinically meaningful mutations with NGS data from cancer cohorts.

• Korean Journal of Microbiology
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• v.43 no.4
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• pp.317-320
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• 2007

In this paper, we have developed base-calling error detection program and algorithm which show the list of the genes or sequences that are suspected to contain base-calling errors. Those programs detect dubious bases in a few aspects in the process of microbial genome project. The first module detects base-calling error from the Phrap file by using contig assembly information. The second module analyzes frame shift mutation if it is originated from real mutation or artifact. Finally, in the case that there is control microbial genome annotation information, the third module extracts and shows the candidate base-calling error list by comparative genome analysis method.