• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.4
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• pp.76-85
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• 1998

Recently, Genetic Algorithm(GA) is widely adopted into a search procedure for structural optimization, which is a stochastic direct search strategy that mimics the process of genetic evolution. This methods consist of three genetics operations maned selection, crossover and mutation. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA, being zero-order method, is very simple. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher probability of converge to global optimum compared to traditional techniques which take one-point search method. In this study, a method of finding the optimum values of suspension parameters is proposed by using the GA. And vehicle is modelled as planar vehicle having 5 degree-of-freedom. The generalized coordinates are vertical motion of passenger seat, sprung mass and front and rear unsprung mass and rotate(pitch) motion of sprung mass. For rapid converge and precluding local optimum, share function which distribute chromosomes over design bound is introduced. Elitist survival model, remainder stochastic sampling without replacement method, multi-point crossover method are adopted. In the sight of the improvement of ride comfort, good result can be obtained in 5-D.O.F. vehicle model by using GA.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.923-928
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• 1993

Genetic algorithms are powerful optimization methods based on the mechanism of natural genetics and natural selection. Genetic algorithms reduce chance of searching local optima unlike most conventional search algorithms and especially show good performances in complex nonlinear optimization problems because they do not require any information except objective function value. This paper presents a new model based on sexual reproduction in nature. In the proposed Sexual Reproduction model(SR model), individuals consist of the diploid of chromosomes, which are artificially coded as binary string in computer program. The meiosis is modeled to produce the sexual cell(gamete). In the artificial meiosis, crossover between homologous chromosomes plays an essential role for exchanging genetic informations. We apply proposed SR model to optimization of the design parameters of Single-sided Linear Induction Motor(SLIM). Sequential Unconstrained Minimization Technique(SUMT) is used to transform the nonlinear optimization problem with many constraints of SLIM to a simple unconstrained problem, We perform optimal design of SLIM available to FA conveyer systems and discuss its results.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.24-29
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• 1997

Recently, Genetic Algorithm(GA), which is a stochastic direct search strategy that mimics the process of genetic evolution, is widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA is very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GA. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher porbability of convergence to global optimum compared to traditional techniques which take one-point search method. The methods consist of three genetics opera- tions named selection, crossover and mutation. In this study, a method of finding the omtimum size and topology of drilling machine is proposed by using the GA, For rapid converge to optimum, elitist survival model,roulette wheel selection with limited candidates, and multi-point shuffle cross-over method are adapted. And pseudo object function, which is the combined form of object function and penalty function, is used to include constraints into fitness function. GA shows good results of weight reducing effect and convergency in optimal design of drilling machine.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.43-54
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• 2004

This study analyzes characteristics and applicability of genetic algorithms and genetic operators to optimize highway alignments. Genetic algorithms, one of artificial intelligence techniques, are fast and efficient search algorithms for generating, evaluation and finding optimal highway alignment alternatives. The performance of genetic algorithms as an optimal search tool highly depends on genetic operators that are designed as a problem-specific. This study adopts low mutation operators(uniform mutation operator, straight mutation operator, non-uniform mutation operator whole non-uniform mutation operator) to explore whole search spaces, and four crossover operators(simple crossover operator, two-point crossover operator, arithmetic crossover operator, heuristic crossover operator) to exploit food characteristics of the best chromosome in previous generations. A case study and a sensitivity analysis have shown that the eight problem-specific operators developed for optimizing highway alignments enhance the search performance of genetic algorithms, and find good solutions(highway alignment alternatives). It has been also found that a mixed and well-combined use of mutation and crossover operators is very important to balance between pre-matured solutions when employing more crossover operators and more computation time when adopting more mutation operators.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.103-112
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• 1999

Genetic algorithm, which uses principles of natural selection and population genetics, is an efficient method to find out an optimal solution. In conventional genetic algorithms, however, the size of gene pool needs to be increased to insure a convergency. Therefore, many memory spaces and much computation time were needed. Also, since child chromosomes were generated by chromosome crossover and gene mutation, the algorithms have a complex structure. Thus, in this paper, a compact stereo matching algorithm using a population-based incremental learning based on probability vector is proposed to reduce these problems. The PBIL method is modified for matching environment. Since th proposed algorithm uses a probability vector and eliminates gene pool, chromosome crossover, and gene mutation, the matching algorithm is simple and the computation load is considerably reduced. Even though the characteristics of images are changed, stable outputs are obtained without the modification of the matching algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.64-70
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• 2002

This paper proposes a new construction method of neural networks. The construction method consists of two fundmental ideas, which are a parallel selection-style evaluation and rules evolution. A new collision avoidance algorithm using genetic and neural network is proposed to avoid moving obstacles such as mobile robots. The input parameters of this algorithm is position of moving obstacles and target. Output is a regenerated direction of mobile robot. This algorithm is very simple and so, it is available to application of real time process. The pattern of collision avoidance is learned through test execution.

• Journal of Korea Society of Industrial Information Systems
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• v.1 no.1
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• pp.29-62
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• 1996

In this paper, the problem of determining cyclic schedules for a material handling hoist in the printed-circuit-board(PCB) electroplating line is considered. The objective of this research is to determine an optimal simple-cycle schedule of the hoist which in turn maximizes the line throughput rate. Previous approaches to the cyclic hoist scheduling problem are all mathematical programming-based approaches to develop cyclic schedules(Mixed Integer Programming, Linear Programming based Branch and Bound, Branch and Bound Search Method and so on). In this paper, a genetic algorithm-based approach for a single hoist scheduling in the PCB electroplating line is described. Through some experiments for the well known example data and randomly generated data, the proposed algorithm is shown to be more efficient than the previous mathematical programming-based algorithm.

• Journal of the Korean Geotechnical Society
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• v.21 no.4
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• pp.115-122
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• 2005

In this paper, a new method composed of discrete element method and genetic algorithm has been introduced to estimate the safety factor and search critical slip surface on slope stability analysis. In case of estimating the safety factor, conventional methods of slope analysis based on the limit equilibrium do not satisfy the overall equilibrium condition; they must make assumptions regarding the inclination and location of the interstice forces. An alternative slope analysis method based on the discrete element method, which can consider the compatibility condition between force and displacement, is presented. Real-coded genetic algorithm is applied to the search for the minimum factor of safety in proposed analysis method. This search method is shown to be more robust than simple optimization routines, which are apt to find local minimum. Examples are also shown to demonstrate the applicability of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• Journal of Korea Water Resources Association
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• v.37 no.3
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• pp.207-217
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• 2004

Our government is interested in the rehabilitation for the old sewer rather than the construction of a new sewer system. However, the research work on the sewer rehabilitation is not sufficient as much as the interest on the rehabilitation is increased. There are some research works for the determination of rehabilitation time by the genetic algorithm in Korea and foreign countries. However, the previous studies have considered the simple elements for the determination of the rehabilitation time and so the complex decision-making according to the degree of sewer superannuation has not been performed. Therefore, in this study, we estimate the capacity and Ⅰ/Ⅰ of sewer and determine the priority of the optimal rehabilitation for each outfall within the draining system. Also we develop the optimal rehabilitation decision making system for the cost estimation of optimal rehabilitation using the genetic algorithm.