• Title, Summary, Keyword: Genetic Algorithm

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

  • Yim, Kook-Hyun;Kim, Jong-Boo;Ahn, Doo-Soo
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.49 no.11
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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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Optimization of Gable Frame Using the Modified Genetic Algorithm (개선된 유전자 알고리즘을 이용한 산형 골조의 최적화)

  • Lee, Hong-Woo
    • Journal of Korean Association for Spatial Structures
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    • v.3 no.4
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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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Shape & Topology Optimum Design of Truss Structures Using Genetic Algorithms (유전자 알고리즘에 의한 평면 및 입체 트러스의 형상 및 위상최적설계)

  • Yuh, Baeg-Youh;Park, Choon-Wook;Kang, Moon-Myung
    • Journal of Korean Association for Spatial Structures
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    • v.2 no.3
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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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Smooth Walking Robot Using Genetic Algorithm (유전알고리즘을 이용한 유연한 보행로봇)

  • 한경수;김상범;김진걸
    • Proceedings of the Korean Society of Precision Engineering Conference
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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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The implementation of the Multi-population Genetic Algorithm using Fuzzy Logic Controller

  • Chun, Hyang-Shin;Kwon, Key-Ho
    • Proceedings of the KAIS Fall Conference
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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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The Migration Scheme between Groups in the Multi-population Genetic Algorithms (다개체군 유전자 알고리즘의 집단간 이주 기법)

  • 차성민;권기호
    • Proceedings of the IEEK Conference
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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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Butter-Worth analog filter parameter estimation using the genetic algorithm (유전자 알고리듬을 이용한 Butter-Worth 아날로그 필터의 파라미터 추정)

  • Son, Jun-Hyeok;Seo, So-Hyeok
    • Proceedings of the KIEE Conference
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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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An Application of a Hybrid Genetic Algorithm on Missile Interceptor Allocation Problem (요격미사일 배치문제에 대한 하이브리드 유전알고리듬 적용방법 연구)

  • Han, Hyun-Jin
    • Journal of the military operations research society of Korea
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    • v.35 no.3
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    • pp.47-59
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    • 2009
  • A hybrid Genetic Algorithm is applied to military resource allocation problem. Since military uses many resources in order to maximize its ability, optimization technique has been widely used for analysing resource allocation problem. However, most of the military resource allocation problems are too complicate to solve through the traditional operations research solution tools. Recent innovation in computer technology from the academy makes it possible to apply heuristic approach such as Genetic Algorithm(GA), Simulated Annealing(SA) and Tabu Search(TS) to combinatorial problems which were not addressed by previous operations research tools. In this study, a hybrid Genetic Algorithm which reinforces GA by applying local search algorithm is introduced in order to address military optimization problem. The computational result of hybrid Genetic Algorithm on Missile Interceptor Allocation problem demonstrates its efficiency by comparing its result with that of a simple Genetic Algorithm.

Automatic generation of Fuzzy Parameters Using Genetic and gradient Optimization Techniques (유전과 기울기 최적화기법을 이용한 퍼지 파라메터의 자동 생성)

  • Ryoo, Dong-Wan;La, Kyung-Taek;Chun, Soon-Yong;Seo, Bo-Hyeok
    • Proceedings of the KIEE Conference
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    • pp.515-518
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    • 1998
  • This paper proposes a new hybrid algorithm for auto-tuning fuzzy controllers improving the performance. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a genetic-MGM algorithm. The object of the proposed algorithm is to promote search efficiency by a genetic and modified gradient optimization techniques. The proposed genetic and MGM algorithm is based on both the standard genetic algorithm and a gradient method. If a maximum point don't be changed around an optimal value at the end of performance during given generation, the genetic-MGM algorithm searches for an optimal value using 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 algorithms. Simulation results verify the validity of the presented method.

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Fast and Scalable Path Re-routing Algorithm Using A Genetic Algorithm (유전자 알고리즘을 이용한 확장성 있고 빠른 경로 재탐색 알고리즘)

  • Lee, Jung-Kyu;Kim, Seon-Ho;Yang, Ji-Hoon
    • The KIPS Transactions:PartB
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    • v.18B no.3
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    • pp.157-164
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    • 2011
  • This paper presents a fast and scalable re-routing algorithm that adapts to dynamically changing networks. The proposed algorithm integrates Dijkstra's shortest path algorithm with the genetic algorithm. Dijkstra's algorithm is used to define the predecessor array that facilitates the initialization process of the genetic algorithm. After that, the genetic algorithm re-searches the optimal path through appropriate genetic operators under dynamic traffic situations. Experimental results demonstrate that the proposed algorithm produces routes with less traveling time and computational overhead than pure genetic algorithm-based approaches as well as the standard Dijkstra's algorithm for large-scale networks.