• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.561-564
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• 2002

With Rapid Prototyping system, the efficient packing in a fixed work volume reduces build time when multiple parts are built in a process. In this paper, an efficient and rapid packing algorithm is developed for SLS system that has cylindrical workspace. A genetic algorithm is implemented to place as many part as possible in a vat. For fast computation, a collision detection algorithm "k-DOPs Tree" is implemented.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.1
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• pp.259-269
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• 2005

This paper describes the application of genetic algorithm for the discrete optimum design of reinforced concrete continuous beams. The objective is to minimize the total cost of reinforced concrete beams including the costs of concrete, form work, main reinforcement and stirrup. The flexural and shear strength, deflection, crack, spacing of reinforcement, concrete cover, upper-lower bounds on main reinforcement, beam width-depth ratio and anchorage for main reinforcement are considered as the constraints. The width and effective depth of beam and steel area are taken as design variables, and those are selected among the discrete design space which is composed with dimensions and steel area being used from in practice. Optimum result obtained from GA is compared with other literature to verify the validity of GA. To show the applicability and efficiency of GA, it is applied to three and five span reinforced concrete beams satisfying with the Korean standard specifications.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.644-649
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• 2006

In this paper, a methodology for estimating the model parameters of a discrete-time system and tuning a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems regarding parameter estimation and controller tuning, pseudo-parallel genetic algorithms(PPGAs) are used. The parameters of a discrete-time system are estimated using both the model adjustment technique and a PPGA. The digital PID controller is described by the pulse transfer function and then its three gains are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.845-857
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• 2003

We explore the use of genetic programming to evolve decision trees directly for classification problems with both discrete and continuous predictors. We demonstrate that the derived hypotheses of standard algorithms can substantially deviated from the optimum. This deviation is partly due to their top-down style procedures. The performance of the system is measured on a set of real and simulated data sets and compared with the performance of well-known algorithms like CHAID, CART, C5.0, and QUEST. Proposed algorithm seems to be effective in handling problems caused by top-down style procedures of existing algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.108-115
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• 2006

This paper presents a watermarking algorithm in the discrete wavelet transform domain using evolutionary algorithm. The proposed algorithm consists of wavelet-domain watermark insertion and genetic algorithm-based watermark extraction. More specifically watermark is inserted to the low-frequency region of wavelet transform domain, and watermark extraction is efficiently performed by using the evolutionary algorithm. The proposed watermarking algorithm is robust against various attacks such as JPEG and JPEG2000 image compression and geometric transformations.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.6
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• pp.33-43
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• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.227-233
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• 2010

In this study, a genetic algorithm was utilized to optimize the stacking sequence of a composite plate subjected to a high velocity impact. The aim is to minimize the maximum backplane displacement of the plate. In the finite element model, we idealized the impactor using solid elements and modeled the composite plate by shell elements to reduce the analysis time. Various tests were carried out to investigate the effect of parameters in the genetic algorithm such as the type of variables, population size, number of discrete variables, and mutation probability.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.99-106
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• 2003

In this paper, a new optimized design methodology is proposed. It integrates the discrete element method (DEM) and real-coded genetic algorithm for the design of landfill lining system subjected to equipment loadings. In applying the design method to a tapered lining system, the effect of the taperness, which means the change of shape for cover soil, is examined. The optimization problem to maximize the capacity of a waste-containment facility is solved using real coded genetic algorithm. Numerical example analysis is carried out for a typical landfill slope structure.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.4
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• pp.147-156
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• 1994

Though optimization method had been used for long time for the optimal design of ship structure, design variables in the most cases were assumed to be continuous real values or it was not easy to solve the mixed integer optimum design problems using the conventional optimization methods. Thus, it was often tried to use various initial starting points to locate the best optimum paint and to use special method such as branch and bound method to handle the discrete design variables in the optimization problems. Sometimes it had succeed, but the essential problems for dealing with the local optimum and discrete design variables was left unsolved. Hence, in this paper, Genetic Algorithms adopting the biological evolution process is applied to the ship structural design problem where the integer values for the number of stiffen design variables or the discrete values for the plate thickness variables would be more preferable in order to find out their effects on the final optimum design. Through the numerical result comparisons, it was found that Genetic Algorithm could always yield the global optimum for the discrete and mixed integer structural optimization problem cases even though it takes more time than other methods.