• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1305-1308
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• 2003

A new technique is presented to construct a predictive model of plasma etch process. This was accomplished by combining a backpropagation neural network (BPNN) and a genetic algorithm (GA). The predictive model constructed in this way is referred to as a GA-BPNN. The GA played a role of controlling training factors simultaneously. The training factors to be optimized are the hidden neuron, training tolerance, initial weight magnitude, and two gradients of bipolar sigmoid and linear functions. Each etch response was optimized separately. The proposed scheme was evaluated with a set of experimental plasma etch data. The etch process was characterized by a $2^3$ full factorial experiment. The etch responses modeled are aluminum (A1) etch rate, silica profile angle, A1 selectivity, and dc bias. Additional test data were prepared to evaluate model appropriateness. The GA-BPNN was compared to a conventional BPNN. Compared to the BPNN, the GA-BPNN demonstrated an improvement of more than 20% for all etch responses. The improvement was significant in the case of A1 etch rate.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.430-438
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• 2004

Among the optimization method, GA (genetic algorithm) is a very powerful searching method enough to compete with design sensitivity analysis method. GA is very easy to apply, since it dose not require any design sensitivity information. However, GA has been computationally not efficient due to huge repetitive computation. In this study, parallel computation is adopted to Improve computational efficiency, Paralleled GA is introduced on a clustered LINUX based personal computer system.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.44-51
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• 2002

In order to generate hull form, fairness criteria applies to object function, B-spline curve vertices are considered as design variables, optimization is proceeded with satisfying geometric constraint conditions. GA(Genetic Algorithm) and optimality criteria apply to optimization process in this study.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.9
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• pp.1039-1048
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• 2015

In this paper, we propose an efficient stair locomotion method for a quadruped robot with mechanism of insectile legs using genetic algorithm(GA). In the proposed method, we first define the factors and the reachable region for the stair locomotion. In addition, we set the gene and the fitness function for GA and generate the gait trajectory by searching the landing position of a quadruped robot, which has the minimun distance of movement and the optimal energy stability margin(ESM). Finally, we verify the effectiveness and superiority of the proposed stair locomotion method through the computer simulations.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.157-160
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• 2001

This paper has been studied a Adaptive Mutation rate Genetic Algorithm Processor. Genetic Algorithm(GA) has some control parameters such as the probability of bit mutation or the probability of crossover. These value give a priori by the designer There exists a wide variety of values for for control parameters and it is difficult to find the best choice of these values in order to optimize the behavior of a particular GA. We proposed a Adaptive mutation rate GA within a steady-state genetic algorithm in order to provide a self-adapting mutation mechanism. In this paper, the proposed a adaptive mutation rate GAP is implemented on the FPGA board with a APEX EP20K600EBC652-3 devices. The proposed a adaptive mutation rate GAP increased the speed of finding optimal solution by about 10%, and increased probability of finding the optimal solution more than the conventional GAP

• Korean Management Science Review
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• v.17 no.2
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• pp.39-54
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• 2000

This paper presents a causal forecasting model using guided genetic algorithm in continuous manufacturing process. The guide genetic algorithm(GGA) is an extended genetic algorithm(GA) using penalty function and population diversity index to increase forecasting accuracy. GGA adds to the canonical GA the concept of a penalty function to avoid selecting the unproductive chromosomes and to make a proper searching direction. Also, GGA modifies the current population using the similarity of chromosomes to avoid falling into the trap of local optimal solution. For investigation GGA performance, we used a set of real data that was collected in local glass melting processes, and experimental results show the proposed model results in the better forecasting accuracy than linear regression model and canonical GA.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.328-333
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• 2010

최적해를 구하는 효과적인 방법 중 하나인 GA (Genetic Algorithm)은 높은 품질의 해를 구하기 위해서 많은 연산시간이 필요하지만, GPU (Graphics Processing Unit)의 높은 데이터 병렬처리 능력과 우수한 부동소수 연산능력을 이용하면 빠르게 처리 가능하다. 이 논문에서는 GPU를 이용하여 가속한 섬 기반의 RVGA (Real-Valued Genetic Algorithm)와 GPU를 이용하지 않는 RVGA를 비교하여 평가하였으며, 또한 GPU를 이용하지만 RVGA가 아닌 Simple GA인 경우와도 비교하여 평가 하였다. 그 결과, GPU를 이용한 경우 속도 향상을 할 수 있었으며, Simple GA보다 RVGA가 더 속도가 향상되었다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.299-299
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• 2000

In this paper, a determination method of number of AGVs fer introducing to the multi-path material handling systems is presented by using genetic algorithm. For serving the raw material to each work stations automatically, there needs to introduce a AGVs for transfer the raw martial. To reduce the overall production cost in the material handling systems, however, a trade off exists between the amount of inventory hold on the shop floor and the number of AGVs needed to provide adequate service. In this paper, firstly a objective function which included the net present fixed costs of each stations and each purchased AGVs, delivering cost. stock inventory cost, and safety stock inventory cost is presented. Secondly by using genetic algorithm, the optimal reorder quantity at each stations is decided, where the number of AGVs is increased step by step. From a simulation with different GA parameters, we can determine a optimal number of AGVs to reduce the overall production cost. Thus, the effectiveness of GA for determining the number of AGVs is verified in automated material handling systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.731-734
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• 2017

GA(Genetic Algorithm)는 자연계 진화 과정의 적자생존의 유전적 부호화 및 처리과정을 모델링함으로서 해석적으로 처리하기 힘든 문제의 최적화에 널리 이용하고 있으며, 퍼지제어에서 룰의 선택에도 적용된다. 본 논문에서는 일반적인 GA방법에 자료의 유사성을 체크하는 방법을 도입하여 Fuzzy Rule선택 환경에 적용하고 시뮬레이션을 통해 이를 확인한다. 시뮬레이션 결과 제안된 SFRGA(Similarity Fuzzy Rule Genetic Algorithm)방법은 일반적 GA방법보다 단축된 지연시간 효과와 부수적으로 조기포화 현상(premature convergence)의 감소 및 자동 배정 퍼지 클리스터링(Fuzzy clustering)의 가능성을 얻을 수 있었다.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2827-2829
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• 1999

Genetic algorithms were described as a method of solving large-scaled optimization problems with complex constraints. It has overcome their slowness, a major drawback of genetic algorithms using hardware implementation of genetic algorithm processor (GAP). In this study, we proposed GAP effectively connecting the goodness of survival-based GA, steady-state GA, tournament selection. Using Pipeline Parallel processing, handshaking protocol effectively, the proposed GAP exhibits 50% speed-up over survival-based GA which runs one million crossovers per second(1MHz). It will be used for high speed processing such of central processor of EHW, robot control and many optimization problem.