• Title/Summary/Keyword: Genetic Algorithm

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A Study on the Dynamics of Genetic Algorithm Based on Stochastic Differential Equation (유전 알고리즘의 확률 미분방정식에 의한 동역학 분석에 대한 연구)

  • 석진욱;조성원
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.10a
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    • pp.296-300
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    • 1997
  • Recently, the genetic algorithm has been applied to the various types of optimization problems and these attempts have very successfully. However, in most cases on these approaches, there is not given by investigator about to the theoritical analysis. The reason that the analysis of the dynamics for genetic algorithm is not clear, is the probablitic aspect of genetic algorithm. In this paper, we investigate the analysis of the internal dynamics for genetic algorithm using stochastic differential method. In addition, we provide a new genetic algorithm, based on the study of the convergence property for the genetic algorithm.

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A Study on Optimization of Manganese Nodule Carrier and its Economic Evaluation (망간단괴 수송선의 최적화와 경제성 평가에 관한 연구)

  • Park, Jae-Hyung;Yoon, Gil-Su
    • Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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    • 2002.10a
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    • pp.40-44
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    • 2002
  • 선박 설계시 최적화에 있어 종래에는 Random search Parametric study, Hook&Jeeves Method등이 사용되어져 왔으나 1960년대 Genetic algorithm이 소개되고 꾸준히 발전함과 함께 선박 설계에서도 Genetic algorithm이 사용되기 시작하였다. 본 논문에서는 이러한 Genetic algorithm 중 Simple Genetic algorithm(SGA), Micro Genetic algorithm(MGA), Threshold Genetic algorithm(TGA), Hybrid Genetic algorithm(HGA)을 선박 설계에 적용하여 그 성능을 비교 검토해 보았다. MGA는 계산 부담을 줄이기 위해 작은 개체로 효율적인 탐색을 하며, TGA는 local optimum에서 쉽게 벗어나게 할 수 있는 특징이 있다. HGA는 Hook&Jeeves Method를 Genetic algorithm과 병합되어 있다. 이를 바탕으로 본 논문에서 망간단괴 수송선의 경제성을 평가한다. 평가 방법은 연간 300만톤을 생산한다고 가정하여 연간 운송 용적을 동호제약으로 해서 최적화를 한 뒤, 이를 이용하여 몇가지 Case로 나누어서 초기 자본, 연간 비용, 20년간 총 비용을 계산하여 가장 경제적인 선박을 선택한다.

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Simulation Optimization of Manufacturing System using Real-coded Genetic Algorithm (실수 코딩 유전자 알고리즘을 이용한 생산 시스템의 시뮬레이션 최적화)

  • Park, Kyoung-Jong
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.28 no.3
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    • pp.149-155
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    • 2005
  • In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

A Study on the Optimal Trajectory Planning for a Ship Using Genetic algorithm (유전 알고리즘을 이용한 선박의 최적 항로 결정에 관한 연구)

  • 이병결;김종화;김대영;김태훈
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.255-255
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    • 2000
  • Technical advance of electrical chart and cruising equipment make it possible to sail without a man. It is important to decide the cruising route in view of effectiveness and stability of a ship. So we need to study on the optimal trajectory planning. Genetic algorithm is a strong optimization algorithm with adaptational random search. It is a good choice to apply genetic algorithm to the trajectory planning of a ship. We modify a genetic algorithm to solve this problem. The effectiveness of the revised genetic algorithm is assured through computer simulations.

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

  • Song, Ho-Jeong;Kim, Hyun-Gi
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.17 no.2
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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.

Nonlinear IIR filter parameter estimation using the genetic algorithm (유전자 알고리듬을 이용한 비선형 IIR 필터의 파라미터 추정)

  • Son, Jun-Hyeok;Seo, Bo-Hyeok
    • Proceedings of the KIEE Conference
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    • 2005.05a
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    • pp.15-17
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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 learning 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 nonlinear IIR 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 nonlinear IIR filter parameter using the genetic algorithm.

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FIR filter parameter estimation using the genetic algorithm (유전자 알고리듬을 이용한 FIR 필터의 파라미터 추정)

  • Son, Jun-Hyeok;Seo, Bo-Hyeok
    • Proceedings of the KIEE Conference
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    • 2005.10b
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    • pp.502-504
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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 learning 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 FIR 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 FIR filter parameter using the genetic algorithm.

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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 s.10
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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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A Mew Genetic Algorithm based on Mendel's law (Mendel의 법칙을 이용한 새로운 유전자 알고리즘)

  • Chung, Woo-Yong;Kim, Eun-Tai;Park, Mignon
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.376-378
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    • 2004
  • Genetic algorithm was motivated by biological evaluation and has been applied to many industrial applications as a powerful tool for mathematical optimizations. In this paper, a new genetic optimization algorithm is proposed. The proposed method is based on Mendel's law, especially dominance and recessive property. Homologous chromosomes are introduced to implement dominance and recessive property compared with the standard genetic algorithm. Because of this property of suggested genetic algorithm, homologous chromosomes looks like the chromosomes for the standard genetic algorithm, so we can use most of existing genetic operations with little effort. This suggested method searches the larger solution area with the less probability of the premature convergence than the standard genetic algorithm.

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