• 제목/요약/키워드: genetic network

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유전자 알고리즘을 이용한 신경망 설계 (Designing Neural Network Using Genetic Algorithm)

  • 박정선
    • 한국정보처리학회논문지
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    • 제4권9호
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    • pp.2309-2314
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    • 1997
  • 본 연구는 보험 회사의 파산 예측을 위하여 신경회로망이 사용되는데 이를 최적화하기 위하여 유전자 알고리즘이 사용된다. 유전자 알고리즘은 최적의 네트워크 구조와 매개변수들을 제시해 준다. 유전자 알고리즘에 의해 설계된 신경회로망은 파산 예측을 함에 있어 discriminant analysis, logistic regression, ID3, CART 등과 비교되는데 가장 좋은 성능을 보여준다.

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유전알고리즘과 신경회로망을 이용한 선형유도전동기의 최적설계 (Optimum Design of a linear Induction Motor using Genetic Algorithm and Neural Network)

  • 김창업
    • 조명전기설비학회논문지
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    • 제17권5호
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    • pp.29-35
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    • 2003
  • 본 논문에서는 유전 알고리즘과 신경 회로망을 이용하여 선형유도전동기의 최적화 설계 방법에 대하여 연구하였다. 최대 추력 및 추력/중량을 목적함수로 하여 유전알고리즘, 신경회로망, 유전알고리즘과 신경회로망의 합성에 의한 방법으로 선형유도전동기의 최적설계를 한 결과 제안한 방법이 가장 우수함을 확인하였다.

인공신경망과 유전 알고리즘을 이용한 팩맨 게임 강화학습 (Pacman Game Reinforcement Learning Using Artificial Neural-network and Genetic Algorithm)

  • 박진수;이호정;황두연;조수선
    • 대한임베디드공학회논문지
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    • 제15권5호
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    • pp.261-268
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    • 2020
  • Genetic algorithms find the optimal solution by mimicking the evolution of natural organisms. In this study, the genetic algorithm was used to enable Pac-Man's reinforcement learning, and a simulator to observe the evolutionary process was implemented. The purpose of this paper is to reinforce the learning of the Pacman AI of the simulator, and utilize genetic algorithm and artificial neural network as the method. In particular, by building a low-power artificial neural network and applying it to a genetic algorithm, it was intended to increase the possibility of implementation in a low-power embedded system.

유전알고리즘을 이용한 부분방전 패턴인식 최적화 연구 (A Study on the Optimization of PD Pattern Recognition using Genetic Algorithm)

  • 김성일;이상화;구자윤
    • 전기학회논문지
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    • 제58권1호
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    • pp.126-131
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    • 2009
  • This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

유전자 알고리즘을 이용한 신뢰 통신망 최적화 (Optimizing Reliable Network using Genetic Algorithm)

  • 이학종;강주락;권기호
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 1999년도 추계종합학술대회 논문집
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    • pp.452-455
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    • 1999
  • Genetic algorithm is well known as the efficient algorithm which can solve a difficult problem. Network design considering reliability is NP-hard problem with cost, distance, and volume. Therefore genetic algorithm is considered as a good method for this problem. This paper suggests the reliable network which can be constructed with minimum cost using genetic algorithm and the rank method based on reliability for improving the performance. This method shows more excellent than existing method and confirms the result through simulation.

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ATM 망에서 최적 가상 경로망 설계를 위한 유전자 알고리즘 응용에 관한 연구 (A Study on Applying Genetic Algorithm for Optimum Virtual Path Network Design in ATM Network)

  • 강주락;권기호
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2000년도 하계종합학술대회 논문집(3)
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    • pp.31-34
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    • 2000
  • Genetic Algorithm is well known as the efficient algorithm which can solve a difficult optimization problem. Recently, there has been increasing interest in applying genetic algorithm to problem related to network design. In this paper, we propose a two-step genetic algorithm for designing a optimum virtual path network(VPN) for a given physical network and traffic demand. The first step is a routing step in which a route is found between every node pair in the network. In the second step, paths are assigned as VPs so as to minimize the total number of VPs configured, the number of VPs carried by a link, and the VP hopcount. We study the performance of the propose algorithm through simulation. The result shows that the VPN generated by the proposed algorithm is good in minimizing the number of VPs configured, the load on a link, and the VP hopcount.

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Enhancement OLSR Routing Protocol using Particle Swarm Optimization (PSO) and Genrtic Algorithm (GA) in MANETS

  • Addanki, Udaya Kumar;Kumar, B. Hemantha
    • International Journal of Computer Science & Network Security
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    • 제22권4호
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    • pp.131-138
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    • 2022
  • A Mobile Ad-hoc Network (MANET) is a collection of moving nodes that communicate and collaborate without relying on a pre-existing infrastructure. In this type of network, nodes can freely move in any direction. Routing in this sort of network has always been problematic because of the mobility of nodes. Most existing protocols use simple routing algorithms and criteria, while another important criterion is path selection. The existing protocols should be optimized to resolve these deficiencies. 'Particle Swarm Optimization (PSO)' is an influenced method as it resembles the social behavior of a flock of birds. Genetic algorithms (GA) are search algorithms that use natural selection and genetic principles. This paper applies these optimization models to the OLSR routing protocol and compares their performances across different metrics and varying node sizes. The experimental analysis shows that the Genetic Algorithm is better compared to PSO. The comparison was carried out with the help of the simulation tool NS2, NAM (Network Animator), and xgraph, which was used to create the graphs from the trace files.

유전 알고리즘의 연산처리를 통한 개선된 경로 탐색 기법 (Improved Route Search Method Through the Operation Process of the Genetic Algorithm)

  • 지홍일;서창진
    • 전기학회논문지P
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    • 제64권4호
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    • pp.315-320
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    • 2015
  • Proposal algorithm in this paper introduced cells, units of router group, for distributed processing of previous genetic algorithm. This paper presented ways to reduce search delay time of overall network through cell-based genetic algorithm. As a result of performance analysis comparing with existing genetic algorithm through experiments, the proposal algorithm was verified superior in terms of costs and delay time. Furthermore, time for routing an alternative path was reduced in proposal algorithm, in case that a network was damaged in existing optimal path algorithm, Dijkstra algorithm, and the proposal algorithm was designed to route an alternative path faster than Dijkstra algorithm, as it has a 2nd shortest path in cells of the damaged network. The study showed that the proposal algorithm can support routing of alternative path, if Dijkstra algorithm is damaged in a network.

GENIE : 신경망 적응과 유전자 탐색 기반의 학습형 지능 시스템 엔진 (GENIE : A learning intelligent system engine based on neural adaptation and genetic search)

  • 장병탁
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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    • pp.27-34
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    • 1996
  • GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

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유전 알고리즘을 이용한 비선형 시스템의 최적 신경 회로망 구조에 관한 연구 (A Study on Optimal Neural Network Structure of Nonlinear System using Genetic Algorithm)

  • 김홍복;김정근;김민정;황승욱
    • 한국항해항만학회지
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    • 제28권3호
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    • pp.221-225
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    • 2004
  • 본 논문은 신경 회로망과 유전 알고리즘을 이용한 비선형 시스템 모델링을 다룬다. 비선형 함수의 근사성 때문에 시스템을 식별하고 제어하기 위해서 신경 회로망을 응용한 연구가 실제로 많이 이루어지고 있다. 빠른 응답시간과 최소의 오차를 위해서는 최적구조 신경 회로망을 설계하는 것이 중요하다. 유선 알고리즘은 최근에 단순성과 견고성 때문에 점점 많이 이용되는 추세이다. 따라서 본 논문에서는 유선알고리즘을 이용하여 신경회로망을 최적화한다. 오차와 응답시간을 최소화하는 신경 회로망 구조를 위해서 유전알고리즘의 유전자로 이진 코딩하여 최적 신경회로망을 탐색하고자 한다. 시뮬레이션을 통해서, 최적 신경회로망 구조가 비선형 시스템 식별에 효과적인 것을 입증하고자 한다.