• Proceedings of the Korean Information Science Society Conference
• /
• 2005.07b
• /
• pp.835-837
• /
• 2005

본 논문에서는 가보 웨이블릿을 통해 얼굴 이미지로부터 특징을 추출하고, 그에 Genetic Algorithm 을 통해 구한 특징점별 가중치를 적용하여 얼굴 인식을 하는 방법을 소개한다. 각 특징점별로 가중치를 적용하는 방법은, 기존의 Gabor wavelet 을 사용한 얼굴 인식 방법들에 비해 높은 인식률을 보인다. 특징점별 가중치들은 진화 알고리즘을 통해 학습 되어진다.

• 전자공학회논문지 IE
• /
• v.46 no.2
• /
• pp.7-10
• /
• 2009

This paper presents the implementation of libraries of hardware modules for genetic algorithm using VHDL. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment. So, it is especially suited to applications where no hardware specifications can be given in advance. Evolvable hardware is based on the idea of combining reconfigurable hardware device with evolutionary computation, such as genetic algorithm. Because of parallel, no function call overhead and pipelining, a hardware genetic algorithm give speedup over a software genetic algorithm. This paper suggests the hardware genetic algorithm for evolvable embedded system chip. That includes simulation results and analysis for several fitness functions. It can be seen that our design works well for the three examples.

• 전자공학회논문지 IE
• /
• v.45 no.4
• /
• pp.27-32
• /
• 2008

This paper presents the implementation of simple genetic algorithm using hardware description language for evolvable hardware embedded system. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment. So, it is especially suited to applications where no hardware specifications can be given in advance. Evolvable hardware is based on the idea of combining reconfigurable hardware device with evolutionary computation, such as genetic algorithm. Because of parallel, no function call overhead and pipelining, a hardware genetic algorithm give speedup over a software genetic algorithm. This paper suggests the hardware genetic algorithm for evolvable embedded system chip. That includes simulation results for several fitness functions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.10a
• /
• pp.783-786
• /
• 2008

외판원문제(Traveling Salesman problem: TSP)는 전형적인 조합최적화 문제로 위치하는 n개의 모든 지점을 오직 한번씩만 방문하는 순회경로를 결정하는 과정에서 순회비용 또는 순회거리를 최소화한다. 따라서 본 논문에서는 종래의 NP-hard문제로 널리 알려진 TSP를 해결하기 위해서 메타 휴리스틱기법 중에서 가장 널리 이용되고 있는 유전 알고리즘(Genetic Algorithm: GA)을 이용한다. 마지막으로, 유전 알고리즘을 이용해 외판원문제에 적합한 성능을 보이는 유전 연산자를 찾아내기 위해 수치 실험을 통해 그 성능에 대한 평가를 한다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.3
• /
• pp.253-258
• /
• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• Journal of the Institute of Electronics Engineers of Korea TE
• /
• v.42 no.1
• /
• pp.1-6
• /
• 2005

Channel routing is a problem assigning each net to a track after global routing and minimizing the track that assigned each net. In this paper we propose a genetic algorithm searching solution space for 4-layer channel routing problem. We compare the performance of proposed genetic algorithm(GA) for channel routing with that of other 4-layer channel routing algorithm by analyzing the results of each implementation.

• Korean Journal of Medicinal Crop Science
• /
• v.20 no.6
• /
• pp.440-446
• /
• 2012

Gastrodia elata, an achlorophyllous orchid plant, is rare medicinal plant. We investigated the genetic diversity in G. elata from 4 locations by using Inter-Simple Sequence Repeats (ISSR) markers. Shannon's information Index (S.I.) indicating genetic diversity ranged from 0.255 (Pocheon) to 0.322 (Muju) with the mean of 0.29. The level of genetic diversity was lower than other plant and most genetic diversity was allocated among individuals within populations (26.81%). The UPGMA dendrogram based on genetic distance failed in showing decisive geographic relationship. In the case of gastrodin (GA), the major components in G. elata, Sangju was highest. The ergothionine (ERG) was detected a lot of contents in Muju and Pocheon. In conclusion, our results is very important information for explaining relationship of genetic variation and functional substances without the effects of environment factors and developing genetic marker by ISSR in G. elata, which may be responsible for the development of breeds with a lot of functional substance in G. elata.

• Journal of Information Technology Services
• /
• v.18 no.5
• /
• pp.155-164
• /
• 2019

With the emergence of new ICT technologies, information security threats are becoming more advanced, intelligent, and diverse. Even though the awareness of the importance of information security increases, the information security budget is not enough because of the lack of effectiveness measurement of the information security investment. Therefore, it is necessary to optimize the information security investment in each business environment to minimize the cost of operating the information security countermeasures and mitigate the damages occurred from the information security breaches. In this paper, using genetic algorithms we propose an investment optimization model for information security countermeasures with the limited budget. The optimal information security countermeasures were derived based on the actual information security investment status of SMEs. The optimal solution supports the decision on the appropriate investment level for each information security countermeasures.

• Journal of the Korea Safety Management & Science
• /
• v.3 no.3
• /
• pp.191-200
• /
• 2001

Parallel Genetic Algorithms partition the whole population into several sub-populations and search the optimal solution by exchanging the information each others periodically. Distributed Genetic Algorithm, one of Parallel Genetic Algorithms, divides a large population into several sub-populations and executes the traditional Genetic Algorithm on each sub-population independently. And periodically promising individuals selected from sub-populations are migrated by following the migration interval and migration rate to different sub-populations. In this paper, for the Travelling Salesman Problems, we analyze and compare with Distributed Genetic Algorithms using different Genetic Algorithms and using same Genetic Algorithms on each separated sub-population The simulation result shows that using different Genetic Algorithms obtains better results than using same Genetic Algorithms in Distributed Genetic Algorithms. This results look like the property of rapidly searching the approximated optima and keeping the variety of solution make interaction in different Genetic Algorithms.

• Asian-Australasian Journal of Animal Sciences
• /
• v.27 no.11
• /
• pp.1548-1553
• /
• 2014

Four Korean native cattle (KNC) breeds-Hanwoo, Chikso, Heugu, and Jeju black-are entered in the Domestic Animal Diversity Information System of the United Nations Food and Agriculture Organization (FAO). The objective of this study was to assess the genetic diversity, phylogenetic relationships and population structure of these KNC breeds (n = 120) and exotic breeds (Holstein and Charolais, n = 56). Thirty microsatellite loci recommended by the International Society for Animal Genetics/FAO were genotyped. These genotypes were used to determine the allele frequencies, allelic richness, heterozygosity and polymorphism information content per locus and breed. Genetic diversity was lower in Heugu and Jeju black breeds. Phylogenetic analysis, Factorial Correspondence Analysis and genetic clustering grouped each breed in its own cluster, which supported the genetic uniqueness of the KNC breeds. These results will be useful for conservation and management of KNC breeds as animal genetic resources.