• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.208-216
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• 2009

A number of estimation of distribution algorithms have been proposed that do not use explicitly crossover and mutation of traditional genetic algorithms, but estimate the distribution of population for more efficient search. But because it is not easy to discover higher-order correlations of variables, lower-order correlations are estimated most cases under various constraints. In this paper, we propose a new estimation of distribution algorithm that represents higher-order correlations of the data and finds global optimum more efficiently. The proposed algorithm represents the higher-order correlations among variables by building random hypergraph model composed of hyperedges consisting of variables which are expected to be correlated, and generates the next population by Bayesian sampling algorithm Experimental results show that the proposed algorithm can find global optimum and outperforms the simple genetic algorithm and BOA(Bayesian Optimization Algorithm) on decomposable functions with deceptive building blocks.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.773-774
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• 2005

The knapsack problem (KP) is one of the traditional optimization problems. Specially, multiconstrained knapsack problem (MKP) is well-known NP-hard problem. Many heuristic algorithms and evolutionary algorithms have tackled this problem and shown good performance. This paper presents a novel genetic algorithm for the multiconstrained knapsack problem. The proposed algorithm is called 'Adaptive Link Adjustment'. It is based on integer random key representation and uses additional ${\alpha}$ and ${\beta}$-process as well as selection, crossover and mutation. The experiment results show that it can be archive good performance.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.116-124
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• 1999

In most conventional design optimization of dynamic system, design sensitivities are utilized. However, design sensitivities based optimization method has numbers of drawback. First, computing design sensitivities for dynamic system is mathematically difficult, and almost impossible for many complex problems as well. Second, local optimum is obtained. On the other hand, Genetic Algorithm is the search technique based on the performance of system, not on the design sensitivities. It is the search algorithm based on the mechanics of natural selection and natural genetics. GA search, differing from conventional search techniques, starts with an initial set of random solutions called a population. Each individual in the population is called a chromosome, representing a solution to the problem at hand. The chromosomes evolve through successive iterations, called generations. As the generation is repeated, the fitness values of chromosomes were maximized, and design parameters converge to the optimal. In this study, Genetic Algorithm is applied to the actual dynamic optimization problems, to determine the optimal design parameters of the dynamic system.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.3
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• pp.54-59
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• 2005

The cylindrical object are widely used as mechanical parts in the manufacturing process. In order to handling those objects using a robot or an automated machine automatically, the pose of the object must be known. The pose can be described by two rotation angles; one angle about the x axis and the other about the y axis. In the many previous researches these angles were obtained by the computationally intensive algorithm, that is, fitting the data as a polynomial and doing pseudo inverse. So that, this method required high performance microprocessor. In this paper in order to avoid complex computation, a new method based on a genetic algorithm is proposed and analyzed through a series of simulations. This algorithm utilized the geometry of the cylindrical shape. The simulation results show that this method find the pose angles very well In most cases, but the computation time is randomly changed because the genetic algorithm is basically one of the random search method.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.324-327
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• 2011

스마트폰과 무선인터넷 상에서 멀티미디어 사용이 일반화되면서, 스마트폰 환경에서 멀티미디어 관련 취약점을 악용한 공격이 급증하고 있다. 이러한 취약점을 악용한 공격을 예방하기 위해, 퍼징(Fuzzing)을 적용하여 취약점을 미리 발견하려는 연구들이 진행되고 있다. 본 논문에서는 '임의 생성 기반 퍼징'(단순랜덤, simple random) 방법과 '구조적 변이 기반 퍼징'(구조적 변이, structured mutation) 방법을, 안드로이드 스마트폰용 멀티미디어 플레이어에 적용하는 실험을 수행하였다. 또한 PC 환경의 멀티미디어 플레이어들에서 발생했던 기존 취약점을 악용한 공격파일(exploit)을 안드로이드 스마트폰 환경의 멀티미디어 플레이어들에 적용하고 그 결과를 분석하였다. 실험 결과, PC 환경에서는 구조적 변이 퍼징이 효과적인데 비해, 안드로이드 스마트폰 환경에서는 단순 랜덤 퍼징이 효과적임을 알 수 있었다.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.753-759
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• 2011

The HIV (Human Immunodeficiency Virus) causes AIDS (Acquired Immune Deficiency Syndrome). The process of infection and mutation by HIV can be described by a 3rd order state equation. For this HIV model that includes the dynamics of the mutant virus, we present a parameter estimation scheme using two state variables sporadically measured, out of the three, by employing a genetic algorithm. It is assumed that these non-uniformly sampled measurements are subject to random noises. The effectiveness of the proposed parameter estimation is demonstrated by simulations. In addition, the estimated parameters are used to analyze the equilibrium points of the HIV model, and the results are shown to be consistent with those previously obtained.

• Economic and Environmental Geology
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• v.28 no.4
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• pp.425-431
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• 1995

Genetic algorithms are so named because they are analogous to biological processes. The model parameters are coded in binary form. The algorithm then starts with a randomly chosen population of models called chromosomes. The second step is to evaluate the fitness values of these models, measured by a correlation between data and synthetic for a particular model. Then, the three genetic processes of selection, crossover, and mutation are performed upon the model in sequence. Genetic algorithms share the favorable characteristics of random Monte Carlo over local optimization methods in that they do not require linearizing assumptions nor the calculation of partial derivatives, are independent of the misfit criterion, and avoid numerical instabilities associated with matrix inversion. An additional advantage over converntional methods such as iterative least squares is that the sampling is global, rather than local, thereby reducing the tendency to become entrapped in local minima and avoiding the dependency on an assumed starting model.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.4
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• pp.67-72
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• 2012

In this paper a new method of handling linear constraints for the genetic algorithm is suggested. The method is designed to maintain the feasibility of offsprings during the evolution process of the genetic algorithm. In the genetic algorithm, the chromosomes are coded as the vectors in the real vector space constrained by the linear constraints. A method of handling the linear constraints already exists in which all the constraints of equalities are eliminated so that only the constraints of inequalities are considered in the process of the genetic algorithm. In this paper a new method is presented in which all the constraints of inequalities are eliminated so that only the constraints of equalities are considered. Several genetic operators such as arithmetic crossover, simplex crossover, simple crossover and random vector mutation are designed so that the resulting offspring vectors maintain the feasibility subject to the linear constraints in the framework of the new handling method.

• Journal of Microbiology and Biotechnology
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• v.20 no.6
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• pp.1018-1021
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• 2010

Metallic and non-metallic isomerases can be used to produce commercially important monosaccharides. To determine which category of isomerase is more suitable as a template for directed evolution to improve enzymes for galactose isomerization, L-arabinose isomerase from Escherichia coli (ECAI; E.C. 5.3.1.4) and tagatose-6-phosphate isomerase from Staphylococcus aureus (SATI; E.C. 5.3.1.26) were chosen as models of a metallic and non-metallic isomerase, respectively. Random mutations were introduced into the genes encoding ECAI and SATI at the same rate, resulting in the generation of 515 mutants of each isomerase. The isomerization activity of each of the mutants toward a non-natural substrate (galactose) was then measured. With an average mutation rate of 0.2 mutations/kb, 47.5% of the mutated ECAIs showed an increase in activity compared with wild-type ECAI, and the remaining 52.5% showed a decrease in activity. Among the mutated SATIs, 58.6% showed an increase in activity, whereas 41.4% showed a decrease in activity. Mutant clones showing a significant change in relative activity were sequenced and specific increases in activity were measured. The maximum increase in activity achieved by mutation of ECAI was 130%, and that for SATI was 190%. Based on these results, the characteristics of the different isomerases are discussed in terms of their usefulness for directed evolution of non-natural substrate isomerization.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.599-617
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• 2006

A network model and a Genetic Algorithm(GA) is proposed to solve the simultaneous estimation of the trip distribution and traffic assignment from traffic counts in the congested networks in a logit-based Stochastic User Equilibrium (SUE). The model is formulated as a problem of minimizing the non-linear objective functions with the linear constraints. In the model, the flow-conservation constraints of the network are utilized to restrict the solution space and to force the link flows meet the traffic counts. The objective of the model is to minimize the discrepancies between the link flows satisfying the constraints of flow-conservation, trip production from origin, trip attraction to destination and traffic counts at observed links and the link flows estimated through the traffic assignment using the path flow estimator in the legit-based SUE. In the proposed GA, a chromosome is defined as a vector representing a set of Origin-Destination Matrix (ODM), link flows and travel-cost coefficient. Each chromosome is evaluated from the corresponding discrepancy, and the population of the chromosome is evolved by the concurrent simplex crossover and random mutation. To maintain the feasibility of solutions, a bounded vector shipment is applied during the crossover and mutation.