• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.223-230
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• 2001

Since self-organizing map (SOM) preserves the topology of ordering in input spaces and trains itself by unsupervised algorithm, it is Llsed in many areas. However, SOM has a shortcoming: structure cannot be easily detcrmined without many trials-and-errors. Structure-adaptive self-orgnizing map (SASOM) which can adapt its structure as well as its weights overcome the shortcoming of self-organizing map: SASOM makes use of structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundmies as close to the class boundaries as possible. In this scheme, the initialization of weights of newly adapted nodes is important. This paper proposes a method which optimizes SASOM with genetic algorithm (GA) to determines the weight vector of newly split node. The leanling algorithm is a hybrid of unsupervised learning method and supervised learning method using LVQ algorithm. This proposed method not only shows higher performance than SASOM in terms of recognition rate and variation, but also preserves the topological order of input patterns well. Experiments with 2D pattern space data and handwritten digit database show that the proposed method is promising.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.752-758
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• 2005

The transportation problem (TP) is known as one of the important problems in Industrial Engineering and Operational Research (IE/OR) and computer science. When the problem is associated with additional fixed cost for establishing the facilities or fulfilling the demand of customers, then it is called fixed charge transportation problem (fcTP). This problem is one of NP-hard problems which is difficult to solve it by traditional methods. This paper aims to show the application of spanning-tree based Genetic Algorithm (GA)approach for solving nonlinear fixed charge transportation problem. Our new idea lies on the GA representation that includes the feasibility criteria and repairing procedure for the chromosome. Several numerical experimental results are presented to show the effectiveness of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.64-66
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• 2003

DNA(Deoxyribo Nucleic Acid)컴퓨팅은 생체분자를 계산의 도구로 이용하는 새로운 계산 방법으로 DNA 정보 저장능력과 DNA의 상보적인 관계를 이용하여 연산을 수행하는 방법이다. 최근에는 DNA 분자들이 갖는 강력한 병렬성을 이용하여 NP-Complete 문제에 적용하는 연구가 많이 시도되고 있다. Adleman이 DNA 컴퓨팅을 이용해 해결한 HPP(Hamilton Path Problem)와는 달리 TSP(Traveling Salesman Problem)는 간선에 가중치가 추가되었기 때문에 DNA 염기배열로 표현하기가 어렵고 또한 염기배열의 길이를 줄이기 위해 고정길이 염기배열을 사용할 경우 가중치가 커지면 효율적이지 못하다. 본 논문에서는 스무딩 알고리즘(smoothing algorithm)을 사용하여 간선의 가중치를 일정한 비율로 줄인 다음 유전자 알고리즘을 사용하여 최적의 염기배열을 찾는 방법을 제안하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.65-74
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• 2005

본 연구는 기계고장 시 대체경로를 고려한 새로운 유사계수와 주어진 기간 내 수요변화를 고려하여 제조 셀을 구성하는 방법론을 개발하는 것이다. 본 연구의 방법론은 2단계로 나누어진다. 1단계에서는 기계고장 시 이용 가능한 대체경로를 고려하여 새로운 유사계수를 제시하고 유전자 알고리즘을 활용하여 부품 군을 식별하는 것이다. 셀 응용의 성패를 좌우하는 주요한 요소들 중 하나는 수요변화에 대한 유연성으로 수요변화, 이용 가능한 기계의 능력 및 납기일에 따라 셀을 재구성하기가 쉬운 일은 아닐 것이다. 대부분의 논문에서 제안한 방법들은 단일기간에 대한 고정 수요를 고려하였으나, 수요의 변화로 인하여 셀 설계는 대부분의 연구에서 고려한 단일기간보다는 장기적인 면을 고려해야 할 것이다. 수요가 변화하는 상황에서 운용요소와 일정요소를 고려한 셀 구성에 대한 새로운 방법론을 2단계에 소개한다.

• Convergence Security Journal
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• v.14 no.7
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• pp.59-64
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• 2014

We investigate $p(n){\cdot}2^{O(\sqrt{n})}$ algorithm for 0/1 knapsack problem where x is the total bit length of a list of sizes of n objects. The algorithm is adaptable of method that achieves a similar complexity for the partition and Subset Sum problem. The method can be applied to other optimization or decision problem based on a list of numerics sizes or weights. 0/1 knapsack problem can be used to solve NP-Complete Problems with pseudo-polynomial time algorithm. We try to apply this technique to bio-informatics problem which has pseudo-polynomial time complexity.

• Journal of the military operations research society of Korea
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• v.34 no.2
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• pp.63-77
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• 2008

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. The innovations are cellular concept, dynamic channel assignment and hierarchical network design. 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. Hierarchical network design using genetic algorithm is the first three-tier (Macro, Micro, Pico) model. We increase the reality through applying to 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 90% of the demand.

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.99-113
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• 2002

The multi-class traffic assignment problem is the most typical one of the multi-solution traffic assignment problems and, recently formulation of the models and the solution algorithm have been received a great deal of attention. The useful solution algorithm, however, has not been proposed while formulation of the multi-class traffic assignment could be performed by adopting the variational inequality problem or the fixed point problem. In this research, we developed a hybrid solution algorithm which combines GA algorithm, diagonal algorithm and clustering algorithm for the multi-class traffic assignment formulated as a variational inequality Problem. GA algorithm and clustering algorithm are introduced for the wide area and small cost. We also performed an experiment with toy network(2 link) and tested the characteristics of the suggested algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.147-154
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• 2013

To provide a method for optimizing the variation range of the received power is applied to Adaptive Genetic Algorithm in a LED communication environment. By optimizing the power distribution dynamically for mobile or fixed using a genetic algorithm, to eliminate the need for a system design that is customized to be independent of the movement pattern of the user's adaptability, and environmental properties. It is possible to improve easily the convenience of the user. The room power deviation from any location can be reduced by reducing the energy. the simulation results, the proposed method does not exist obstacles in an empty room with power deviation $10.5{\mu}W$ decreased 10 percent to reduce the deviation of the received power is shown that. In comparison with conventional methods, convergence to the optimal value is improved, the genetic algorithm proposed was confirmed to be efficient in terms of energy savings.

• Journal of Korea Multimedia Society
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• v.4 no.1
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• pp.82-90
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• 2001

Genetic Algorithm(GA) has known as a method of solving NP problem in various applications. Since a major drawback of the GA is that it needs a long computation time, the hardware implementation of Genetic Algorithm is focused on in recent studies. This paper proposes a new type of embedded one chip computer fort Hardware Implementation of Genetic Algorithm. The proposed embedded one chip computer consists of 16 Bit CPU care and hardware of genetic algorithm. In contrast to conventional hardware oriented GA which is dependent on main computer in the process of GA, the proposed embedded one chip computer is independent on main computer. Conventional hardware GA uses the fixed length of chromosome but the proposed embedded one chip computer uses the variable length of chromosome by employing the efficient 16 bit Pipeline Unit. Experimental results show that the proposed one chip computer is applicable to the design of evolvable hardware for Random NRZ bit synchronization circuit.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.552-557
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• 2000

In the paper, we proposed a face recognition method that uses GA-BP(Genetic Algorithm-Back propagation Network) that optimizes initial parameters such as bias values or weights. Each pixel in the picture is used for input of the neuralnetwork. The initial weights of neural network is consist of fixed-point real values and converted to bit string on purpose of using the individuals that arte expressed in the Genetic Algorithm. For the fitness value, we defined the value that shows the lowest error of neural network, which is evaluated using newly defined adaptive re-learning operator and built the optimized and most advanced neural network. Then we made experiments on the face recognition. In comparison with learning convergence speed, the proposed algorithm shows faster convergence speed than solo executed back propagation algorithm and provides better performance, about 2.9% in proposed method than solo executed back propagation algorithm.