• 한국CDE학회논문집
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• 제1권3호
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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.

• 한국식품영양학회지
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• 제9권3호
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• pp.294-298
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• 1996

By the 505 chromotest which utilized Escherichia bolt PQ 37, Korean medicinal plants had been screened to Investigate the antimutagenic effect to aflatoxin B1(AFBl). Ikmocho(IMC, Leonurus sibiricus L.) was extracted with hot water. The extract was not found to be mutagenic in the Salmonella mutation test with or without metabolic activation, and the extract was showed to possess the antimutagenic properties towards AFB1-induced metation. The mutagenicity of AFB1 was inhibited by methanol soluble fracstion (IMC-MS) in dose-dependent. However, water-soluble fraction exhibited comutagenic activity. The greatest inhibitory effect of IMC-MS on AFB1 mutagenicity occurred when IMC-MS was first incubated, AFB1 followed by a second incubation with the cells and 59 mixture. Also lower inhibition was occurred when S9 mixtures were first incubated, with IMC-MS followed by a second incubation with AFBI. The results of the sequential incubation study support the probability that one mechanism of inhibition could involve the formation of chemical complex between IMC-MS and AFB1 rather than deactivation of S9 enzyme.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.310-315
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• 2005

This paper proposes a fast optimization method by combining queen-bee evolution and derivative evaluation in genetic algorithms. These two operations make it possible for genetic algorithms to focus on highly fitted individuals and rapidly evolved individuals, respectively. Even though the two operations can also increase the probability that genetic algorithms fall into premature convergence phenomenon, that can be controlled by strong mutation rates. That is, the two operations and the strong mutation strengthen exploitation and exploration of the genetic algorithms, respectively. As a result, the genetic algorithm employing queen-bee evolution and derivative evaluation finds optimum solutions more quickly than those employing one of them. This was proved by experiments with one pattern matching problem and two function optimization problems.

• 한국컴퓨터정보학회논문지
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• 제16권12호
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• pp.23-31
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• 2011

최적화 알고리즘의 하나로 제안한 단순 박테리아협동 최적화는 비교적 좋은 성능을 보였으나 개체가 한 번에 한 스텝씩 움직이는 것으로 말미암아 성능에 한계가 발생하였다. 이러한 문제를 해결하고자 개체에 등급을 매기고 등급별로 개체의 속력을 할당하는 방법을 제안하여 어느 정도의 성능향상을 보았다. 본 논문에서는 개체에 속력을 할당하는 방법에 추가적으로 성능향상을 위하여 기존의 진화적 최적화 알고리즘들이 많이 사용한 돌연변이를 새로 추가한 알고리즘을 제안한다. 새로 추가한 돌연변이에서는 적합도가 좋지 않은 일정 퍼센트의 개체를 해당 개체의 등급에 비례하는 영역내로 돌연변이를 일으킨다. 즉, 적합도가 낮아 등급이 낮으면 더 큰 표준편차의 가우시안 잡음을 섞어서 돌연변이를 발생한다. 결국 낮은 등급을 갖는 개체들은 부모로부터 멀리 떨어질 확률이 증가하게 된다. 이렇게 함으로서 개체가 지역 최적해 영역에 빠질 가능성을 줄이고 지역 최적해 영역에 빠져도 빠르게 나올 수 있는 가능성이 높아진다. 네개의 함수 최적화 문제에 적용해본 결과 개체 속력과 돌연변이를 함께 적용했을 경우에 성능이 향상되는 것을 보았다. 다만, 아주 복잡도가 높은 함수에서는 반드시 좋아지지 만은 않았는데, 추후 이를 해결하기 위한 다른 방법을 고안해야할 것으로 판단된다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권4호
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• pp.115-124
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• 2021

기능 블록 다이어그램(Function Block Diagram, FBD) 프로그램이 안전 필수 시스템 구현에 널리 사용되면서 FBD 프로그램에 대한 효과적인 테스트가 중요해졌다. 뮤테이션 테스팅은 오류 기반 테스팅 기술로, 오류 탐지에 매우 효과적이지만 비용이 많이 든다. 본 연구에서는 FBD 프로그램 테스터를 지원하기 위한, FBD 프로그램 대상 자동 뮤턴트 생성기를 제안한다. MuGenFBD 도구는 뮤턴트 생성 비용과 동등 뮤턴트 문제를 고려하여 설계되었다. MuGenFBD 도구의 성능을 평가하기 위해 실제 산업 사례에 대한 실험을 수행한 결과, MuGenFBD를 활용하여 뮤턴트 생성 시 동등 뮤턴트를 생성할 비율이 낮으며 적은 비용으로 FBD 프로그램 대상 뮤턴트를 효과적으로 자동 생성할 수 있음을 확인하였다. 제안하는 도구는 FBD 프로그램에 대한 뮤테이션 분석 및 뮤테이션 충분성 기준을 만족시키는 테스트 생성을 효과적으로 지원할 수 있다.

• 대한산업공학회지
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• 제38권3호
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• pp.173-181
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• 2012

The size and layout of user interface components need to be optimally designed in terms of reachability, visibility, clearance, and compatibility in order for efficient and effective use of products. The present study develops an ergonomic design method which optimizes the size and layout of user interface components using adaptive genetic algorithm. The developed design method determines a near-optimal design which maximizes the aggregated score of 4 ergonomic design criteria (reachability, visibility, clearance, and compatibility). The adaptive genetic algorithm used in the present study finds a near-optimum by automatically adjusting the key parameter (probability of mutation) of traditional genetic algorithm according to the characteristic of current solutions. Since the adaptive mechanism partially helps to overcome the local optimality problem, the probability of finding the near-optimum has been substantially improved. To evaluate the effectiveness of the developed design method, the present study applied it to the user interface design for a portable wireless communication radio.

• Smart Structures and Systems
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• 제15권3호
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• 제어로봇시스템학회논문지
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• 제16권6호
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• pp.572-578
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• 2010

In this paper, the final fitness results of the satellite mission scheduling algorithm, which is designed by using the genetic algorithm, are simulated and compared with respect to the control constants. Heuristic algorithms, including the genetic algorithm, are good to find global optima, however, we have to find the optimal control constants before its application to a problem, because the algorithm is strongly effected by the control constants. In this research, the satellite mission scheduling algorithm is simulated with different crossover probability and mutation probability, which is major control constant of the genetic algorithm.

• ETRI Journal
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• 제29권1호
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• pp.95-98
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• 2007

A low-profile phased array antenna with a low sidelobe was designed and fabricated using a genetic algorithm (GA). The subarray distances were optimized by GA with chromosomes of 78 bits, a population of 100, a crossover probability of 0.9, and a mutation probability of 0.005. The array antenna has 24 subarrays in 14 rows, and is designed as a mobile terminal for Ku-band satellite communication. The sidelobe level was suppressed by 6.5 dB after optimization, compared to the equal spacing between subarrays. The sidelobe level was verified from the far-field pattern measurement by using the fabricated array antenna with optimized distance.

• Journal of the korean society of oceanography
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• 제32권4호
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• pp.163-170
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• 1997

Some CMP gathers acquired from shallow marine seismic reflection survey in offshore Korea do not show the hyperbolic trend of moveout. It originated from so-called swell effect of source and streamer, which are towed under rough sea surface during the data acquisition. The observed time deviations of NMO-corrected traces can be entirely ascribed to the swell effect. To correct these time deviations, a residual statics is introduced using Genetic Algorithms (GA) into the swell correction. A new class of global optimization methods known as GA has recently been developed in the field of Artificial Intelligence and has a resemblance with the genetic evolution of biological systems. The basic idea in using GA as an optimization method is to represent a population of possible solutions or models in a chromosome-type encoding and manipulate these encoded models through simulated reproduction, crossover and mutation. GA parameters used in this paper are as follows: population size Q=40, probability of multiple-point crossover P$_c$=0.6, linear relationship of mutation probability P$_m$ from 0.002 to 0.004, and gray code representation are adopted. The number of the model participating in tournament selection (nt) is 3, and the number of expected copies desired for the best population member in the scaling of fitness is 1.5. With above parameters, an optimization run was iterated for 101 generations. The combination of above parameters are found to be optimal for the convergence of the algorithm. The resulting reflection events in every NMO-corrected CMP gather show good alignment and enhanced quality stack section.