• Journal of the Korean Statistical Society
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• 제29권4호
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• pp.407-422
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• 2000

This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.81-84
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• 1998

Genetic Algorithms (GAs) are stochastic algorithms whose search methods model some natural phenomena. The procedure of GAs may be divided into two sub-procedures : Operation and Selection. Chromosomes can produce new offspring by means of operation, and the fitter chromosomes can produce more offspring than the less fit ones by means of selection. However, operation which is executed randomly and has some limits to its execution can not guarantee to produce fitter chromosomes. Thus, we propose a method which gives a directional information to the genetic operator by reinforcement learning. It can be achived by using neural networks to apply reinforcement learning to the genetic operator. We use the amount of fitness change which can be considered as reinforcement signal to calcualte the error terms for the output units. Then the weights are updated using backpropagtion algorithm. The performance improvement of GAs using reinforcement learning can be measured by applying the pr posed method to GA-hard problem.

• Food Science and Biotechnology
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• 제17권6호
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• pp.1352-1356
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• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

• 대한토목학회논문집
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• 제34권3호
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• pp.821-831
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• 2014

추계학적 강수발생 및 모의기법은 수문학적 모형의 입력 자료로써 널리 이용되고 있다. 그러나 Modified Bartlett-Lewis Rectangular Pulse(MBLRP)와 같은 추계학적 포아송 클러스터 강수생성 모형에 대해서 국부최적화 방법을 통한 매개변수 추정 방법은 매개변수의 신뢰성에 상당한 영향을 주는 것으로 알려져 있다. 최근에는 MBLRP 모형의 국부해추정 문제를 해소하기 위하여 Particle Swarm Optimization (PSO) 또는 Shuffled Complex Evolution developed at The University of Arizona (SCE-UA) 등 매개변수 추정 성능이 우수한 전역최적화기법이 도입되고 있지만, 제한된 매개변수 공간에서 항상 신뢰성 있는 매개변수 추정이 가능한 것은 아니다. 뿐만 아니라, 모형의 매개변수들이 갖고 있는 불확실성에 관한 연구는 아직 충분히 논의되지 않았다. 이러한 관점에서 본 연구는 Bayesian 기법과 연계한 MBLRP 모형을 개발하였으며 각 매개변수들의 사후분포(Posterior Distribution)를 유도하여 매개변수가 내포하는 불확실성을 정량적으로 평가하였다. 그 결과 관측값에 대한 시간단위 이하 강수발생 통계치를 효과적으로 복원하고 있음을 확인할 수 있었다.

• 한국정밀공학회지
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• 제14권3호
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• pp.89-97
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• 1997

A genetic algorithm (GA) is a stochastic direct search strategy that mimics the process of genetic evolution. The GA applied herein works on a population of structural designs at any one time, and uses a structured information exchange based on the principles of natural selection and wurvival of the fittest to recombine the most desirable features of the designs over a sequence of generations until the process converges to a "maximum fitness" design. Principles of genetics are adapted into a search procedure for structural optimization. The methods consist of three genetics operations mainly named selection, cross- over and mutation. In this study, a method of finding the optimum topology of truss/beam structure is pro- posed by using the GA. In order to use GA in the optimum topology problem, chromosomes to FEM elements are assigned, and a penalty function is used to include constraints into fitness function. The results show that the GA has the potential to be an effective tool for the optimal design of structures accounting for sizing, geometrical and topological variables.variables.

• 한국전산구조공학회논문집
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• 제15권4호
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• pp.591-598
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• 2002

공학적 설계에 있어 많은 문제들은 몇 가지 목적함수들을 동시에 최소화하여야 할 필요가 있을 경우가 있다. 선박설계에 있어, 종래에는 자재비 경감과 재화중량 증가를 위해 최소중량설계가 구조 설계의 주된 목적이었으나, 값싼 노동력을 내세운 후발 조선국과의 치열한 국제 경쟁을 극복하기 위해서는 보다 경제성 있는 선박 건조 기술 개발이 선행되어야 할 것이다. 이에 따라 본 연구에서는 다목적함수 최적화기법을 이용한 선체 구조의 보다 합리적인 설계 방안에 대한 연구를 수행하여 실제 건조된 유조선을 대상으로 중량, 건조비 등의 경제성을 비교 평가하였다. 다목적 함수로는 유조선의 중량과 건조비로 하였으며 최적화 기법으로는 확률론적 탐색법인 ES(Evolution Strategies)를 이용하였다. 건조비 모델은 상대 건조비 개념을 도입하였고, 종강도 부재는 선급규정에 의해, 횡강도 및 횡격벽 부재는 직접해석법인 일반화된 경사처짐법을 사용하여 설계에 적용하였다. 다목적함수 최적화 결과로부터 도출된 Pareto 최적 설계점들에 대하여, 요구운임률을 각각 산정함으로써 이들 최적 설계점들 중에서 가장 경제성이 뛰어난 선박 설계 방안을 제시하였다.

• 경영과정보연구
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• 제2권
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• pp.75-93
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• 1998

In this paper, we propose a new algorithm(N_BP) to be capable of overcoming limitations of the traditional backpropagation(O_BP). The N_BP is based on the method of conjugate gradients and calculates learning parameters through the line search which may be characterized by order statistics and golden section. Experimental results showed that the N_BP was definitely superior to the O_BP with and without a stochastic term in terms of accuracy and rate of convergence and might surmount the problem of local minima. Furthermore, they confirmed us that the stagnant phenomenon of learning in the O_BP resulted from the limitations of its algorithm in itself and that unessential approaches would never cured it of this phenomenon.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2000년도 춘계학술대회 논문집
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• pp.96-103
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• 2000

This research is a new design method, which will be presented as a basic concept for a more efficient minimum weight design of grillages, as an attempt to describe true collapse mechanism in as overall search as possible. It serves as introduction to the numerical technique of Linear Programming(LP) and Automatic Modified Direct Plastic Frame Analysis(AMDPFA). Attention is directed to both analysis and design, and emphasis is placed on the physical significance of Systematic Searching Techniques(SST) involved. In weight minimum grillages design, the parameterisation study in optimum beam configuration which was carried out over the range of beam sections for a given plastic section modulus likely to occur in structures by suing an adaptive stochastic optimisation technique, Genetic Algorithms.

• 전자공학회논문지B
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• 제32B권12호
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• pp.1680-1686
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• 1995

GAs, effective stochastic search algorithms based on the model of natural evolution and genetics, have been successfully applied to various optimization problems. When population size is not large, GAs often suffer from the phenomenon of premature convergence in which all chromosomes in the population lose the diversity of genes before they find the optimal solution. In this paper, we propose that a new heuristic that maintains the diversity of genes by adding some chromosomes with random mutation and selective mutation into population during evolution. And population size changes dynamically with supplement of new chromosomes. Experimental results for several test functions show that when population size is rather small and the length of chromosome is not long, this method is effective.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1997년도 가을 학술발표회 논문집
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• pp.163-170
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• 1997

The present paper describes an investigation into the application of the genetic algorithm(GA) in the optimization of structural design. Stochastic processes generate an initial population of designs and then apply principles of natural selection/survival of the fittest to improve the designs. The five test functions are used to verify the robustness and reliability of GA, and as a numerical example, minimum weight of a cantilever composite laminated beam with a mix of continuous, integer and discrete design variables is obtained by using GA with exterior penalty function method. The design problem has constraints on strength, displacements, and natural frequencies, and is formulated to a multidimensional nonlinear form. From the results, it is found that the GA search technique is very effective at finding the good optimum solution as well as has higher robustness.