• Korean Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.234-241
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• 1996

Recently, Genetic Algorithms(GAs), which consist of genetic operators named selection crossover and mutation, are widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GAs are very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GAs. So, they can be easily applicable to wide territory of design optimization problems. Also, virtue to multi-point search procedure, they have higher probability of convergence to global optimum compared with traditional techniques which take one-point search method. The introduction of basic theory on GAs, and the application examples in combination optimization of ten-member truss structure are presented in this paper.

• Architectural research
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• v.11 no.2
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• pp.19-26
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• 2009

The effect of rebirthing technique on the genetic algorithm (GA)-based size optimization is investigated. The GA mimics the principles of nature and it can gradually improve structural design through biological operations such as fitness, selection, crossover and mutation. However, premature optimum has been often detected in the generic GA with continuous design variable. Since then, the so-called rebirthing technique has been proposed to avoid this problem. However, the performance of the rebirthing technique has not been reported. Therefore, the size optimizations of spatial structures are tackled to investigate the performance of the rebirthing technique on the generic GA. From numerical results, it is well proved that the rebirthing technique is very effective to produce the optimum values regardless of the values of parameters used in the GA operations.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.724-729
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• 2001

This paper discusses the composition of the theory of reinforcement learning, which is applied in real-time learning, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

• Proceedings of the Korean Society of Developmental Biology Conference
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• 2003.10a
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• pp.94-94
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• 2003

Rotavirus는 유아나 어린아이들에게 가장 일반적으로 나타나는 심한 위장염의 원인자이며 설사로 인한 심한 탈수 증세를 일으켜 급속히 성장하는 유아의 균형적인 영양 공급을 방해함으로써 유아들의 발육과 성장 그리고 심하면 생명에 커다란 영향을 미치게 된다. 한편 모유로 키운 유아들은 설사병의 낮은 발병율과 연관이 있었다. 특히 모유의 뮤신 복합체는 rotavirus에 특이적으로 결합하여 항 바이러스활동을 보여주는 것으로 나타났다. 이러한 배경에서 본 연구는 human breast tissue로부터 lactadherin의 cloning 및 sequence 분석을 통하여 유전자의 다양성을 조사하기로 하였다. 한국 여성 9명의 유두 근처 조직에서 lactadherin을 cloning하여 그 sequence를 보고 된 서양여성의 염기서열과 비교 분석결과 여러 곳에서 single nucleotide variation이 발견되었고 본 연구에서 클론한 lactadherin(31bp-1518bp)의 염기서열과 보고된 서양여성 lactadherin gene의 SNP와 비교하였을 때 8개의 SNP중 3부분만이 일치한다는 것을 확인하였다. 또한 같은 조직중 정상 조직과 암 조직 부분에서 각각 lactadherin을 클론하여 염기서열을 비교 분석하였는데 정상 조직에서 2곳의 silent mutation있었고 암조직에서 2곳의 mutation과 1곳의 silent mutation을 발견하였으며 전체 적으로 정상조직과 암 조직 부분에서 lactadherin을 clone하여 염기서열을 분석해본 결과 암조직일수록 유전자의 변이 비율이 높다는 것을 알 수 있었다. 그리고 동일한 염기서열 상에서 많은 변이가 일어났는데 286dp(A->C), 1418dp(G->C)은 mutation이었고 327dp (A->G), 454(C->T)은 silent mutation이었다. 그 외 DNA상에서 여러 부근에 변이가 존재하였는데 이 결과로 보아 coding region에 위치한 cSNP 중 amino acid 변화를 일으켜 protein structure 또는 function에 영향을 줄 수 있는 non-synonymous cSNP 일 것으로 예상되어지며 natural selection의 영향을 받고 있음을 암시하고 있다. 본 연구에서 관찰되어진 각각의 염기 서열의 변이는 한국 사람이 가지는 lactadherin gene의 cSNP의 일부라고 판단하였다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.55-61
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• 2020

Genetic Algorithm(GA) is applied to a problem that could not figure out its solution in a straightway. It is called as NP-hard problem. GA requires a high-performance system to be run on since the high-cost operations are needed such as crossover, selection, and mutation. Moreover, the scale of the problem domain is normally huge. That is why the straightway cannot be applied. To reduce the drawback of high-cost requirements, we try to answer if all the operations including mutation are necessary for all cases. In the experiment, we set up two cases of with/without mutation operations and gather the number of generations and the fitness of a solution. The subject in the experiment is Travelling Salesman Problem(TSP), which is one of the popular problems solved by GA. As a result, the cases with mutation operation are not faster and the solution is fitter than the case with mutation operation. From the result, the conclusion is that mutation operation does not always need for a better solution in a faster way.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1210-1218
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• 2011

GA (Genetic Algorithms) are efficient for searching for global optima but may have some problems such as premature convergence, convergence to local extremum and divergence. These phenomena are related to the evolutionary operators. As population diversity converges to low value, the search ability of a GA decreases and premature convergence or converging to local extremum may occur but population diversity converges to high value, then genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we analyze the effects of the selection operator, crossover operator, and mutation operator on convergence properties, and propose the sweeping method of mutation probability and elitist propagation rate to maintain the diversity of the GA's population for getting out of the premature convergence. Results of simulation studies verify the feasibility of using these sweeping operators to avoid premature convergence and convergence to local extrema.

• Tuberculosis and Respiratory Diseases
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• v.75 no.5
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• pp.181-187
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• 2013

The emergence of new therapeutic agents for non-small cell lung cancer (NSCLC) implies that histologic subtyping and molecular predictive testing are now essential for therapeutic decisions. Histologic subtype predicts the efficacy and toxicity of some treatment agents, as do genetic alterations, which can be important predictive factors in treatment selection. Molecular markers, such as epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement, are the best predictors of response to specific tyrosine kinase inhibitor treatment agents. As the majority of patients with NSCLC present with unresectable disease, it is therefore crucial to optimize the use of tissue samples for diagnostic and predictive examinations, particularly for small biopsy and cytology specimens. Therefore, each institution needs to develop a diagnostic approach requiring close communication between the pulmonologist, radiologist, pathologist, and oncologist in order to preserve sufficient biopsy materials for molecular analysis as well as to ensure rapid diagnosis. Currently, personalized medicine in NSCLC is based on the histologic subtype and molecular status. This review summarizes strategies for tissue acquisition, histologic subtyping and molecular analysis for predictive testing in NSCLC.

• Journal of Construction Engineering and Project Management
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• v.7 no.2
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• pp.1-10
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• 2017

The Clonal Selection Algorithm (CSA) is an algorithm inspired by the human immune system mechanism. In CSA, several parameters needs to be optimized by large amount of sensitivity analysis for the optimal results. They limit the accuracy of the results due to the uncertainty and subjectivity. Adaptive Clonal Selection (ACS), a modified version of CSA, is developed as an algorithm without controls by pre-defined parameters in terms of selection process and mutation strength. In this paper, we discuss the ACS in detail and present its implementation in construction site utilization planning (CSUP). When applied to a developed model published in research literature, it proves that the ACS are capable of searching the optimal layout of temporary facilities on construction site based on the result of objective function, especially when the parameterization process is considered. Although the ACS still needs some improvements, obtaining a promising result when working on a same case study computed by Genetic Algorithm and Electimze algorithm prove its potential in solving more complex construction optimization problems in the future.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.151-166
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• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3163-3181
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• 2023

Eavesdropping attacks have seriously threatened network security. Attackers could eavesdrop on target nodes and link to steal confidential data. In the traditional network architecture, the static routing path and the important nodes determined by the nature of network topology provide a great convenience for eavesdropping attacks. To resist monitoring attacks, this paper proposes a random routing mutation defense method based on dynamic path weight (DPW-RRM). It utilizes network centrality indicators to determine important nodes in the network topology and reduces the probability of important nodes in path selection, thereby distributing traffic to multiple communication paths, achieving the purpose of increasing the difficulty and cost of eavesdropping attacks. In addition, it dynamically adjusts the weight of the routing path through network state constraints to avoid link congestion and improve the availability of routing mutation. Experimental data shows that DPW-RRM could not only guarantee the normal algorithmic overhead, communication delay, and CPU load of the network, but also effectively resist eavesdropping attacks.