• IEIE Transactions on Smart Processing and Computing
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• 제6권3호
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• pp.151-157
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• 2017

Management optimization is very important in tourism, especially when it is related to productivity. One of the problems in management optimization is tour guide assignment. Well-arranged tour guide assignment will increase productivity while maintaining service quality. A cellular genetic algorithm is one of the methods that can be used to solve this problem. Furthermore, previous study has shown that a cellular dimension increase can lead to promising benefits for certain problems. The objective of this research is to give a clear understanding of the advantages of increasing cellular dimensionality on the tour guide assignment problem by using a cellular genetic algorithm.

• 한국정밀공학회지
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• 제16권6호
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• pp.197-208
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• 1999

This paper presents an operation sequence-based approach for determining machine cell layout in a cellular manufacturing environment. The proposed model considers the sequence of operations in evaluating the intercell and intracell movements. In this paper, design of cellular layout has an objective of minimization of total material flow among facilities, where the total material flow is defined as a weighted sum of both intercell and intracell part movements. The proposed algorithm is developed by using genetic algorithm and can be used to design an optimal cellular layout which can cope with changes of shop floor situation by considering constraints such as the number of machine cells and the number of machines in a machine cell.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.255-258
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• 2001

This paper is implementation of cellular automata neural network system which is a living creatures' brain using evolving hardware concept. Cellular automata neural network system is based on the development and the evolution, in other words, it is modeled on the ontogeny and phylogeny of natural living things. The proposed system developes each cell's state in neural network by CA. And it regards code of CA rule as individual of genetic algorithm, and evolved by genetic algorithm. In this paper we implement this system using evolving hardware concept Evolving hardware is reconfigurable hardware whose configuration is under the control of an evolutionary algorithm. We design genetic algorithm process for evolutionary algorithm and cells in cellular automata neural network for the construction of reconfigurable system. The effectiveness of the proposed system is verified by applying it to time-series prediction.

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 2001년도 Bulletin of The 5th Asian Design Conference International Symposium on Design Science
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• pp.1.2-1
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• 2001

• Structural Engineering and Mechanics
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• 제16권5호
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• pp.557-580
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• 2003

Laminated composite structures find wide range of applications in many branches of technology. They are much suited for weight sensitive structures (like aircraft) where thinner and lighter members made of advanced fiber reinforced composite materials are used. The orientations of fiber direction in layers and number of layers and the thickness of the layers as well as material of composites play a major role in determining the strength and stiffness. Thus the basic design problem is to determine the optimum stacking sequence in terms of laminate thickness, material and fiber orientation. In this paper, a new optimization technique called Cellular Automata (CA) has been combined with Genetic Algorithm (GA) to develop a different search and optimization algorithm, known as Cellular Genetic Algorithm (CGA), which considers the laminate thickness, angle of fiber orientation and the fiber material as discrete variables. This CGA has been successfully applied to obtain the optimal fiber orientation, thickness and material lay-up for multi-layered composite hybrid beams plates and shells subjected to static buckling and dynamic constraints.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.10-14
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• 2004

When providing flexible data transmission for future CDMA(Code Division Multiple Access) cellular networks, problems arise in two aspects: transmission rate. This paper has proposed an approach to maximize the cellular network capacity by combining the genetic transmission rate allocation and a rapid power control algorithm. We present a genetic chromosome representation to express call drop numbers and transmission rate to control mobile's transmission power levels while handling their flexible transmission rates. We suggest a rapid power control algorithm, which is based on optimal control theory and Steffenson acceleration technique comparing with the existing algorithms. Computer simulation results showed effectiveness and efficiency of the proposed algorithm Conclusively, our proposed scheme showed high potential for increasing the cellular network capacity and it can be the fundamental basis of future research.

• 한국정보과학회논문지:소프트웨어및응용
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• 제28권9호
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• pp.669-680
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• 2001

유전자 알고리즘(GA:Genetic Algorithm)은 최적화 문제를 풀기 위해 생물학적 진화(evolution) 과정을 모방한 최적화 알고리즘이다. 유전자 알고리즘은 복잡한 상태 공간에서 최적 해를 찾기 위해 전통적인 최적화 기법과는 달리 유향적 임의 탐색을 행한다. 학습에 해당하는 국부 탐색(local search)을 유전적 알고리즘은 exploration 탐색과 exploitation 탐색의 균형을 유지시켜 줄 수 있는 한 방법이다. 모집단 내의 각 개체가 진화 과정 중에 학습한 유전적 특질들은 그 다음 세대에서 되물림 되며 이러한 학습(learning) 과정을 유전자 알고리즘과 결합시킴으로써 탐색 속도의 향상을 기대할 수 있다. 이 논문에서는 함수 최적화를 위해 속도를 개선한 셀룰러 학습을 기반으로 하는 유전자 알고리즘을 제안한다. 제안하는 셀룰러 학습 전략은 셀룰러 오토마타의 주기성과 수렴성을 기반으로 하며, 유기체가 그 개체의 생명 주기의 한 세대에서 얻게되는 지식과 경험들을 자손에게 전달한다는 이론을 바탕으로 한다. 제안한 셀룰러 학습 전략의 효율을 기존의 복합 유전자 알고리즘에서의 라마키안 진화 및 볼드윈 효과와 비교하였다. 다양한 테스트 베드 함수에 대한 실험을 통하여 셀룰러 학습에 의한 개체의 국부적 향상이 전체적인 성능 향상에 기여함을 알 수 있었고 제안한 학습 전략이 기존의 방법보다 더 빨리 전역 최적 해를 찾을 수 있음을 증명하였다.

• 한국정보과학회논문지:정보통신
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• 제29권2호
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• pp.165-173
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• 2002

In the static channel assignment scheme for cellular mobile computing environment, each cell has a fixed number of channels and supports a service for a user′s request entering to the cell. This scheme has an advantage of simplicity. However, this scheme has a disadvantage that can′t control far hot cell problem. Therefore, to solve this problem, the "channel borrowing" concept is needed. In this paper, we propose a load balancing(channel borrowing, channel reassignment) approach using genetic algorithm. The purposes of using genetic algorithm in this paper are ${\circled1}$ to find early a cell including an available channel and ${\circled2}$ to decrease a number of probings and ${\circled3}$ to migrate to the cell after searching an available channel near upon optimality. To represent effectiveness of the proposed algorithm, we simulated various experiments.

• 한국정밀공학회지
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• 제13권4호
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• pp.143-151
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• 1996

The concept of cellular manufacturing is to decompose a manufacturing system into subsystems, which are easier to manage than the entire manufacturing system. The objective of cellular manufacturing is to group parts with similar processing requirements into part families and machines into cells which meet the processing needs of part families assigned to them. This paper presents a methodology for cell formation based on genetic algorithm which produces improved cell formation in terms of total moves, which is a weighted sum of both intercell moves and intracell moves. A sample problem is solved for two, three and four cells with an approach based on genetic algorithms.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2005년도 추계학술대회 및 정기총회
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• pp.321-333
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• 2005

With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict free channel assignment with the minimum channel span is NP hard. As demand for services has expanded in the cellular segment, sever innovations have been made in order to increase the utilization of bandwidth. The innovations are cellular concept, dynamic channel assignment and hierarchical network design. Hierarchical network design holds the public eye because of increasing demand and quality of service to mobile users. We consider the frequency assignment problem and the base station placement simultaneously. Our model takes the candidate locations emanating from this process and the cost of assigning a frequency, operating and maintaining equipment as an input. In addition, we know the avenue and demand as an assumption. We propose the network about the profit maximization. This study can apply to GSM(Global System for Mobile Communication) which has 70% portion in the world. Hierarchical network design using GA(Genetic Algorithm) is the first three-tier (Macro, Micro, Pico) model, We increase the reality through applying to EMC (Electromagnetic Compatibility Constraints). Computational experiments on 72 problem instances which have 15${\sim}$40 candidate locations demonstrate the computational viability of our procedure. The result of experiments increases the reality and covers more than 90% of the demand.