• 제목/요약/키워드: Genetic Algorithm

검색결과 4,750건 처리시간 0.034초

어댑티드 회로 배치 유전자 알고리즘의 설계와 구현 (Design and Implementation of a Adapted Genetic Algorithm for Circuit Placement)

  • 송호정;김현기
    • 디지털산업정보학회논문지
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    • 제17권2호
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    • pp.13-20
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    • 2021
  • Placement is a very important step in the VLSI physical design process. It is the problem of placing circuit modules to optimize the circuit performance and reliability of the circuit. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for circuit placement include the cluster growth, simulated annealing, integer linear programming and genetic algorithm. In this paper we propose a adapted genetic algorithm searching solution space for the placement problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of each implementation. As a result, it was found that the adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.

유전 알고리즘과 No Fit Polygon법을 이용한 임의 형상 부재 최적배치 연구 (A Study on the Irregular Nesting Problem Using Genetic Algorithm and No Fit Polygon Methodology)

  • 유병항;김동준
    • 한국해양공학회지
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    • 제18권2호
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    • pp.77-82
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    • 2004
  • The purpose of this study is to develop a nesting algorithm, using a genetic algorithm to optimize nesting order, and modified No Fit Polygon(NFP) methodology to place parts with the order generated from the previous genetic algorithm. Various genetic algorithm techniques, which have thus far been applied to the Travelling Salesman Problem, were tested. The partially mapped crossover method, the inversion method for mutation, the elitist strategy, and the linear scaling method of fitness value were selected to optimize the nesting order. A modified NFP methodology, with improved searching capability for non-convex polygon, was applied repeatedly to the placement of parts according to the order generated from previous genetic algorithm. Modified NFP, combined with the genetic algorithms that have been proven in TSP, were applied to the nesting problem. For two example cases, the combined nesting algorithm, proposed in this study, shows better results than that from previous studies.

월쉬변환영역 유전자 알고리즘에 의한 능동소음제어 (Acitve Noise Control via Walsh Transform Domain Genetic Algorithm)

  • 임국현;김종부;안두수
    • 대한전기학회논문지:시스템및제어부문D
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    • 제49권11호
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    • pp.610-616
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    • 2000
  • This paper presents an active noise control algorithm via Walsh transform domain controller learned by genetic algorithm. Typical active noise control algorithms such as the filtered-x lms algorithm are based on the gradient algorithm. Gradient algorithm have two major problems; local minima and eigenvalue ratio. To solve these problems, we propose a combined algorithm which consist of genetic learning algorithm and discrete Walsh transform called Walsh Transform Domain Genetic Algorithm(WTDGA). Analyses and computer simulations on the effect of Walsh transform to the genetic algorithm are performed. The results show that WTDGA increase convergence speed and reduce steady state errors.

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유전 알고리듬을 이용한 자동 동조 퍼지 제어기의 하이브리드 최적화 기법 (Hybrid Optimization Techniques Using Genetec Algorithms for Auto-Tuning Fuzzy Logic Controllers)

  • 유동완;이영석;박윤호;서보혁
    • 대한전기학회논문지:전력기술부문A
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    • 제48권1호
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    • pp.36-43
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    • 1999
  • This paper proposes a new hybrid genetic algorithm for auto-tuning fuzzy controllers improving the performance. In general, fuzzy controllers use pre-determined moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a hybrid genetic algorithm. The object of the proposed algorithm is to promote search efficiency by the hybrid optimization technique. The proposed hybrid genetic algorithm is based on both the standard genetic algorithm and a modified gradient method. If a maximum point is not be changed around an optimal value at the end of performance during given generation, the hybrid genetic algorithm searches for an optimal value using the the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algoritms. Simulation results verify the validity of the presented method.

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유전알고리즘을 이용한 유연한 보행로봇 (Smooth Walking Robot Using Genetic Algorithm)

  • 한경수;김상범;김진걸
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 2002년도 춘계학술대회 논문집
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    • pp.450-453
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    • 2002
  • This paper is concerned with smooth walking robot using genetic algorithm. The new walking algorithm is proposed and we simulated and experimented the algorithm. We suggested the leg trajectory algorithm and balancing trajectory algorithm by applying genetic algorithm. First the leg trajectory algorithm generated the smooth trajectory. Also the balancing trajectory generated the optimal trajectory. We compared results with the previous walking algorithm. It showed that the new proposed algorithm generated the better walking trajectory.

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개선된 유전자 알고리즘을 이용한 산형 골조의 최적화 (Optimization of Gable Frame Using the Modified Genetic Algorithm)

  • 이홍우
    • 한국공간구조학회논문집
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    • 제3권4호
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    • pp.59-67
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    • 2003
  • Genetic algorithm is one of the best ways to solve a discrete variable optimization problem. Genetic algorithm tends to thrive in an environment in which the search space is uneven and has many hills and valleys. In this study, genetic algorithm is used for solving the design problem of gable structure. The design problem of frame structure has some special features(complicate design space, many nonlinear constrants, integer design variables, termination conditions, special information for frame members, etc.), and these features must be considered in the formulation of optimization problem and the application of genetic algorithm. So, 'FRAME operator', a new genetic operator for solving the frame optimization problem effectively, is developed and applied to the design problem of gable structure. This example shows that the new opreator has the possibility to be an effective frame design operator and genetic algorithm is suitable for the frame optimization problem.

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유전자 알고리즘에 의한 평면 및 입체 트러스의 형상 및 위상최적설계 (Shape & Topology Optimum Design of Truss Structures Using Genetic Algorithms)

  • 여백유;박춘욱;강문명
    • 한국공간구조학회논문집
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    • 제2권3호
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    • pp.93-102
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    • 2002
  • The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

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다개체군 유전자 알고리즘의 집단간 이주 기법 (The Migration Scheme between Groups in the Multi-population Genetic Algorithms)

  • 차성민;권기호
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2000년도 추계종합학술대회 논문집(3)
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    • pp.9-12
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    • 2000
  • Genetic algorithm is a searching method which based on the law of the survival of the fittest. Multi-population Genetic Algorithm is a modified form of Genetic Algorithm, which was devised for covering the defect of general genetic algorithm. The core of multi-population genetic algorithm is said to be the migration schemes. The fitness-based migration scheme and the random migration scheme are currently used. In this paper, a new migration scheme, ‘the migration scheme between groups’, is suggested, and compared to the general two migration schemes.

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The implementation of the Multi-population Genetic Algorithm using Fuzzy Logic Controller

  • Chun, Hyang-Shin;Kwon, Key-Ho
    • 한국산학기술학회:학술대회논문집
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    • 한국산학기술학회 2003년도 Proceeding
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    • pp.80-83
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    • 2003
  • A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

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유전자 알고리듬을 이용한 Butter-Worth 아날로그 필터의 파라미터 추정 (Butter-Worth analog filter parameter estimation using the genetic algorithm)

  • 손준혁;서보혁
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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    • pp.2513-2515
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    • 2005
  • Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for leaming in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of Butter-Worth analog filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate Butter-Worth analog filter parameter using the genetic algorithm.

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