• 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.

• The Korean Journal of Food And Nutrition
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• v.9 no.3
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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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• v.5 no.4
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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.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.23-31
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• 2011

The simple bacteria cooperative optimization (sBCO) algorithm that we developed as one of optimization algorithms has shown relatively good performances, but their performances were limited by step-by-step movement of individuals at a time. In order to solve this problem, we proposed a new method that assigned a speed to each individual according to its rank and it was confirmed that it improved the performances of sBCO in some degree. In addition to the assigning of speed to the individuals, we employed a new mutation operation that most existing evolutionary algorithms used in order to enhance the performances of sBCO in this paper. A specific percent of bad individuals are mutated within an area that is proportion to the rank of the individual in the mutation operation. That is, Gaussian noise of large standard deviation is added as the fitness of individuals is low. From this, the probability that the individuals with lower ranks can be located far from its parent will be increased. This causes that the probability of falling into local optimum areas is decreased and the probability of fast escaping the local optimum areas is increased. From experimental results with four function optimization problems, we showed that the performances of sBCO with mutation operation and individual speed were increased. If the optimization function is quite complex, however, the performances are not always better. We should devise a new method for solving this problem as a further work.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.115-124
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• 2021

Since function block diagram (FBD) programs are widely used to implement safety-critical systems, effective testing for FBD programs has become important. Mutation testing, a fault-based testing, is highly effective in fault detection but computationally expensive. To support testers for FBD programs, we propose an automated mutant generator for FBD programs. We designed the MuGenFBD tool with the cost and equivalent mutant issues in consideration. We conducted experiments on real industrial examples to present the performance of MuGenFBD. The results show that MuGenFBD can generate mutants for FBD programs automatically with low probability of equivalent mutants and low cost. This tool can effectively support mutation analysis and mutation-adequate test generation for FBD programs.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.3
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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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• v.15 no.3
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.6
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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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• v.29 no.1
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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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• v.32 no.4
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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.