• Title, Summary, Keyword: 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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    • 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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A Genetic Algorithm-based Scheduling Method for Job Shop Scheduling Problem (유전알고리즘에 기반한 Job Shop 일정계획 기법)

  • 박병주;최형림;김현수
    • Korean Management Science Review
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    • v.20 no.1
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    • pp.51-64
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    • 2003
  • The JSSP (Job Shop Scheduling Problem) Is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. we design scheduling method based on SGA (Single Genetic Algorithm) and PGA (Parallel Genetic Algorithm). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling method based on genetic algorithm are tested on five standard benchmark JSSPs. The results were compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement at a solution. The superior results indicate the successful Incorporation of generating method of initial population into the genetic operators.

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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    • 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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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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    • 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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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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    • 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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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.

Effective Robot Path Planning Method based on Fast Convergence Genetic Algorithm (유전자 알고리즘의 수렴 속도 향상을 통한 효과적인 로봇 길 찾기 알고리즘)

  • Seo, Min-Gwan;Lee, Jae-Sung;Kim, Dae-Won
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.4
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    • pp.25-32
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    • 2015
  • The Genetic algorithm is a search algorithm using evaluation, genetic operator, natural selection to populational solution iteratively. The convergence and divergence characteristic of genetic algorithm are affected by selection strategy, generation replacement method, genetic operator when genetic algorithm is designed. This paper proposes fast convergence genetic algorithm for time-limited robot path planning. In urgent situation, genetic algorithm for robot path planning does not have enough time for computation, resulting in quality degradation of found path. Proposed genetic algorithm uses fast converging selection strategy and generation replacement method. Proposed genetic algorithm also uses not only traditional crossover and mutation operator but additional genetic operator for shortening the distance of found path. In this way, proposed genetic algorithm find reasonable path in time-limited situation.

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

  • 유병항;김동준
    • Journal of Ocean Engineering and Technology
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    • v.18 no.2
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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.

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

  • Ryoo, Dong-Wan;Lee, Young-Seog;Park, Youn-Ho;Seo, Bo-Hyeok
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.1
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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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